Development and Validation of a Post-Radiotherapy Prediction Model for Bowel Dysfunction After Rectal Cancer Resection.

BACKGROUND & AIMS: The benefit of radiotherapy for rectal cancer is based largely on a balance between a decrease in local recurrence and an increase in bowel dysfunction. Predicting postoperative disability is helpful for recovery plans and early intervention. We aimed to develop and validate a risk model to improve the prediction of major bowel dysfunction after restorative rectal cancer resection with neoadjuvant radiotherapy using perioperative features. METHODS: Eligible patients more than 1 year after restorative resection following radiotherapy were invited to complete the low anterior resection syndrome (LARS) score at 3 national hospitals in China. Clinical characteristics and imaging parameters were assessed with machine learning algorithms. The post-radiotherapy LARS prediction model (PORTLARS) was constructed by means of logistic regression on the basis of key factors with proportional weighs. The accuracy of the model for major LARS prediction was internally and externally validated. RESULTS: A total of 868 patients reported a mean LARS score of 28.4 after an average time of 4.7 years since surgery. Key predictors for major LARS included the length of distal rectum, anastomotic leakage, proximal colon of neorectum, and pathologic nodal stage. PORTLARS had a favorable area under the curve for predicting major LARS in the internal dataset (0.835; 95% CI, 0.800-0.870, n = 521) and external dataset (0.884; 95% CI, 0.848-0.921, n = 347). The model achieved both sensitivity and specificity >0.83 in the external validation. In addition, PORTLARS outperformed the preoperative LARS score for prediction of major events. CONCLUSIONS: PORTLARS could predict major bowel dysfunction after rectal cancer resection following radiotherapy with high accuracy and robustness. It may serve as a useful tool to identify patients who need additional support for long-term dysfunction in the early stage. CLINICALTRIALS: gov, number NCT05129215.

China special issue on gastrointestinal tumors-Radiological features of pathological complete response in mismatch repair deficient colorectal cancer after neoadjuvant PD-1 blockade: A post hoc analysis of the PICC phase II trial.

Neoadjuvant programmed cell death protein 1 (PD-1) blockade exhibits promising efficacy in patients with mismatch repair deficient (dMMR) colorectal cancer (CRC). However, discrepancies between radiological and histological findings have been reported in the PICC phase II trial (NCT03926338). Therefore, we strived to discern radiological features associated with pathological complete response (pCR) based on computed tomography (CT) images. Data were obtained from the PICC trial that included 36 tumors from 34 locally advanced dMMR CRC patients, who received neoadjuvant PD-1 blockade for 3 months. Among the 36 tumors, 28 (77.8%) tumors achieved pCR. There were no statistically significant differences in tumor longitudinal diameter, the percentage change in tumor longitudinal diameter from baseline, primary tumor sidedness, clinical stage, extramural venous invasion status, intratumoral calcification, peritumoral fat infiltration, intestinal fistula and tumor necrosis between the pCR and non-pCR tumors. Otherwise, tumors with pCR had smaller posttreatment tumor maximum thickness (median: 10 mm vs 13 mm, P = .004) and higher percentage decrease in tumor maximum thickness from baseline (52.9% vs 21.6%, P = .005) compared to non-pCR tumors. Additionally, a higher proportion of the absence of vascular sign (P = .003, odds ratio [OR] = 25.870 [95% CI, 1.357-493.110]), nodular sign (P < .001, OR = 189.000 [95% CI, 10.464-3413.803]) and extramural enhancement sign (P = .003, OR = 21.667 [2.848-164.830]) was observed in tumors with pCR. In conclusion, these CT-defined radiological features may have the potential to serve as valuable tools for clinicians in identifying patients who have achieved pCR after neoadjuvant PD-1 blockade, particularly in individuals who are willing to adopt a watch-and-wait strategy.

CT-based deep learning model for the prediction of DNA mismatch repair deficient colorectal cancer: a diagnostic study.

BACKGROUND: Stratification of DNA mismatch repair (MMR) status in patients with colorectal cancer (CRC) enables individual clinical treatment decision making. The present study aimed to develop and validate a deep learning (DL) model based on the pre-treatment CT images for predicting MMR status in CRC. METHODS: 1812 eligible participants (training cohort: n = 1124; internal validation cohort: n = 482; external validation cohort: n = 206) with CRC were enrolled from two institutions. All pretherapeutic CT images from three dimensions were trained by the ResNet101, then integrated by Gaussian process regression (GPR) to develop a full-automatic DL model for MMR status prediction. The predictive performance of the DL model was evaluated using the area under the receiver operating characteristic curve (AUC) and then tested in the internal and external validation cohorts. Additionally, the participants from institution 1 were sub-grouped by various clinical factors for subgroup analysis, then the predictive performance of the DL model for identifying MMR status between participants in different groups were compared. RESULTS: The full-automatic DL model was established in the training cohort to stratify the MMR status, which presented promising discriminative ability with the AUCs of 0.986 (95% CI 0.971-1.000) in the internal validation cohort and 0.915 (95% CI 0.870-0.960) in the external validation cohort. In addition, the subgroup analysis based on the thickness of CT images, clinical T and N stages, gender, the longest diameter, and the location of tumors revealed that the DL model showed similar satisfying prediction performance. CONCLUSIONS: The DL model may potentially serve as a noninvasive tool to facilitate the pre-treatment individualized prediction of MMR status in patients with CRC, which could promote the personalized clinical-making decision.

A Longitudinal MRI-Based Artificial Intelligence System to Predict Pathological Complete Response After Neoadjuvant Therapy in Rectal Cancer: A Multicenter Validation Study.

BACKGROUND: Accurate prediction of response to neoadjuvant chemoradiotherapy is critical for subsequent treatment decisions for patients with locally advanced rectal cancer. OBJECTIVE: To develop and validate a deep learning model based on the comparison of paired MRI before and after neoadjuvant chemoradiotherapy to predict pathological complete response. DESIGN: By capturing the changes from MRI before and after neoadjuvant chemoradiotherapy in 638 patients, we trained a multitask deep learning model for response prediction (DeepRP-RC) that also allowed simultaneous segmentation. Its performance was independently tested in an internal and 3 external validation sets, and its prognostic value was also evaluated. SETTINGS: Multicenter study. PATIENTS: We retrospectively enrolled 1201 patients diagnosed with locally advanced rectal cancer who underwent neoadjuvant chemoradiotherapy before total mesorectal excision. Patients had been treated at 1 of 4 hospitals in China between January 2013 and December 2020. MAIN OUTCOME MEASURES: The main outcome was the accuracy of predicting pathological complete response, measured as the area under receiver operating curve for the training and validation data sets. RESULTS: DeepRP-RC achieved high performance in predicting pathological complete response after neoadjuvant chemoradiotherapy, with area under the curve values of 0.969 (0.942-0.996), 0.946 (0.915-0.977), 0.943 (0.888-0.998), and 0.919 (0.840-0.997) for the internal and 3 external validation sets, respectively. DeepRP-RC performed similarly well in the subgroups defined by receipt of radiotherapy, tumor location, T/N stages before and after neoadjuvant chemoradiotherapy, and age. Compared with experienced radiologists, the model showed substantially higher performance in pathological complete response prediction. The model was also highly accurate in identifying the patients with poor response. Furthermore, the model was significantly associated with disease-free survival independent of clinicopathological variables. LIMITATIONS: This study was limited by its retrospective design and absence of multiethnic data. CONCLUSIONS: DeepRP-RC could be an accurate preoperative tool for pathological complete response prediction in rectal cancer after neoadjuvant chemoradiotherapy. UN SISTEMA DE IA BASADO EN RESONANCIA MAGNTICA LONGITUDINAL PARA PREDECIR LA RESPUESTA PATOLGICA COMPLETA DESPUS DE LA TERAPIA NEOADYUVANTE EN EL CNCER DE RECTO UN ESTUDIO DE VALIDACIN MULTICNTRICO: ANTECEDENTES:La predicción precisa de la respuesta a la quimiorradioterapia neoadyuvante es fundamental para las decisiones de tratamiento posteriores para los pacientes con cáncer de recto localmente avanzado.OBJETIVO:Desarrollar y validar un modelo de aprendizaje profundo basado en la comparación de resonancias magnéticas pareadas antes y después de la quimiorradioterapia neoadyuvante para predecir la respuesta patológica completa.DISEÑO:Al capturar los cambios de las imágenes de resonancia magnética antes y después de la quimiorradioterapia neoadyuvante en 638 pacientes, entrenamos un modelo de aprendizaje profundo multitarea para la predicción de respuesta (DeepRP-RC) que también permitió la segmentación simultánea. Su rendimiento se probó de forma independiente en un conjunto de validación interna y tres externas, y también se evaluó su valor pronóstico.ESCENARIO:Estudio multicéntrico.PACIENTES:Volvimos a incluir retrospectivamente a 1201 pacientes diagnosticados con cáncer de recto localmente avanzado y sometidos a quimiorradioterapia neoadyuvante antes de la escisión total del mesorrecto. Eran de cuatro hospitales en China en el período entre enero de 2013 y diciembre de 2020.PRINCIPALES MEDIDAS DE RESULTADO:Los principales resultados fueron la precisión de la predicción de la respuesta patológica completa, medida como el área bajo la curva operativa del receptor para los conjuntos de datos de entrenamiento y validación.RESULTADOS:DeepRP-RC logró un alto rendimiento en la predicción de la respuesta patológica completa después de la quimiorradioterapia neoadyuvante, con valores de área bajo la curva de 0,969 (0,942-0,996), 0,946 (0,915-0,977), 0,943 (0,888-0,998), y 0,919 (0,840-0,997) para los conjuntos de validación interna y las tres externas, respectivamente. DeepRP-RC se desempeñó de manera similar en los subgrupos definidos por la recepción de radioterapia, la ubicación del tumor, los estadios T/N antes y después de la quimiorradioterapia neoadyuvante y la edad. En comparación con los radiólogos experimentados, el modelo mostró un rendimiento sustancialmente mayor en la predicción de la respuesta patológica completa. El modelo también fue muy preciso en la identificación de los pacientes con mala respuesta. Además, el modelo se asoció significativamente con la supervivencia libre de enfermedad independientemente de las variables clinicopatológicas.LIMITACIONES:Este estudio estuvo limitado por el diseño retrospectivo y la ausencia de datos multiétnicos.CONCLUSIONES:DeepRP-RC podría servir como una herramienta preoperatoria precisa para la predicción de la respuesta patológica completa en el cáncer de recto después de la quimiorradioterapia neoadyuvante. (Traducción-Dr. Felipe Bellolio ).

Development and Validation of an Image-based Deep Learning Algorithm for Detection of Synchronous Peritoneal Carcinomatosis in Colorectal Cancer.

OBJECTIVE: The aim of this study was to build a SVM classifier using ResNet-3D algorithm by artificial intelligence for prediction of synchronous PC. BACKGROUND: Adequate detection and staging of PC from CRC remain difficult. METHODS: The primary tumors in synchronous PC were delineated on preoperative contrast-enhanced computed tomography (CT) images. The features of adjacent peritoneum were extracted to build a ResNet3D + SVM classifier. The performance of ResNet3D + SVM classifier was evaluated in the test set and was compared to routine CT which was evaluated by radiologists. RESULTS: The training set consisted of 19,814 images from 54 patients with PC and 76 patients without PC. The test set consisted of 7837 images from 40 test patients. The ResNet-3D spent only 34 seconds to analyze the test images. To increase the accuracy of PC detection, we have built a SVM classifier by integrating ResNet-3D features with twelve PC-specific features (P < 0.05). The ResNet3D + SVM classifier showed accuracy of 94.11% with AUC of 0.922 (0.912-0.944), sensitivity of 93.75%, specificity of 94.44%, positive predictive value (PPV) of 93.75%, and negative predictive value (NPV) of 94.44% in the test set. The performance was superior to routine contrast-enhanced CT (AUC: 0.791). CONCLUSIONS: The ResNet3D + SVM classifier based on deep learning algorithm using ResNet-3D framework has shown great potential in prediction of synchronous PC in CRC.

Correlation Between the Distance to Mesorectal Fascia and Prognosis of cT3 Rectal Cancer: Results of a Multicenter Study From China.

BACKGROUND: The cT3 substage criteria based on extramural depth of tumor invasion in rectal cancer have several limitations. OBJECTIVE: This study proposed that the distance between the deepest tumor invasion and mesorectal fascia on pretherapy MRI can distinguish the prognosis of patients with cT3 rectal cancer. DESIGN: This is a cohort study. SETTING: This study included a prospective, single-center, observational cohort and a retrospective, multicenter, independent validation cohort. PATIENT: Patients who had cT3 rectal cancer with negative mesorectal fascia undergoing neoadjuvant chemoradiotherapy followed by radical surgery were included in 4 centers in China from January 2013 to September 2014. INTERVENTION: Baseline MRI with the distance between the deepest tumor invasion and mesorectal fascia, extramural depth of tumor invasion, and mesorectum thickness were measured. MAIN OUTCOME MEASURES: The cutoff of the distance between the deepest tumor invasion and mesorectal fascia was determined by time-dependent receiver operating characteristic curves, supported by a 5-year progression rate from the prospective cohort, and was then validated in a retrospective cohort. RESULTS: There were 124 and 274 patients included in the prospective and independent validation cohorts. The distance between the deepest tumor invasion and mesorectal fascia was the only predictor for cancer-specific death (HR, 0.1; 95% CI, 0.0-0.7) and was also a significant predictor for distant recurrence (HR, 0.4; 95% CI, 0.2-0.9). No statistically significant difference was observed in prognosis between patients classified as T3a/b and T3c/d. LIMITATIONS: The sample size is relatively small, and the study focused on cT3 rectal cancers with a negative mesorectal fascia. CONCLUSIONS: A cutoff of 7 mm of the distance between the deepest tumor invasion and mesorectal fascia on baseline MRI can distinguish cT3 rectal cancer from a different prognosis. We recommend using the distance between the deepest tumor invasion and mesorectal fascia on baseline MRI for local and systemic risk assessment and providing a tailored schedule of neoadjuvant treatment. See Video Abstract at http://links.lww.com/DCR/B682.CORRELACIÓN ENTRE LA DISTANCIA DE LA FASCIA MESORRECTAL Y EL PRONÓSTICO DEL CÁNCER DE RECTO cT3: RESULTADOS DE UN ESTUDIO MULTICÉNTRICO DE CHINAANTECEDENTES:Los criterios de subestadificación cT3 basados en la profundidad extramural de invasión tumoral en el cáncer de recto tienen varias limitaciones.OBJETIVO:Este estudio propuso que la distancia entre la invasión tumoral más profunda y la fascia mesorrectal en la resonancia magnética preterapia puede distinguir el pronóstico de los pacientes con cT3.DISEÑO:Estudio de cohorte.ENTORNO CLINICO:El estudio incluyó una cohorte observacional, prospectiva, unicéntrica, y una cohorte de validación retrospectiva, multicéntrica e independiente.PACIENTE:Se incluyeron pacientes con cáncer de recto cT3 con fascia mesorrectal negativa sometidos a quimio-radioterapia neoadyuvante seguida de cirugía radical en cuatro centros de China desde enero de 2013 hasta septiembre de 2014.INTERVENCIÓN:Imágenes de resonancia magnética de referencia fueron medidas con la distancia entre la invasión tumoral más profunda y la fascia mesorrectal; la profundidad extramural de la invasión tumoral y el grosor del mesorrecto.PRINCIPALES MEDIDAS DE VALORACION:El límite de la distancia entre la invasión tumoral más profunda y la fascia mesorrectal se determinó mediante curvas características operativas del receptor dependientes del tiempo y se apoyó en la tasa de progresión a 5 años de la cohorte prospectiva, y luego se validó en una cohorte retrospectiva.RESULTADOS:Se incluyeron 124 y 274 pacientes en la cohorte de validación prospectiva e independiente, respectivamente. La distancia entre la invasión tumoral más profunda de la fascia mesorrectal fue el único predictor de muerte específica por cáncer (Hazard ratio: 0.1, 95% CI, 0,0-0,7); y también fue un predictor significativo de recurrencia distante Hazard ratio: 0,4, 95% CI, 0,2-0,9). No se observaron diferencias estadísticamente significativas en el pronóstico entre los pacientes clasificados como T3a/b y T3c/d.LIMITACIONES:El tamaño de la muestra es relativamente pequeño y el estudio se centró en los cánceres de recto cT3 con fascia mesorrectal negativa.CONCLUSIONES:Un límite de 7 mm de distancia entre la invasión tumoral más profunda y la fascia mesorrectal en la resonancia magnética de referencia puede distinguir el cáncer de recto cT3 de diferentes pronósticos. Recomendamos la distancia entre la invasión tumoral más profunda y la fascia mesorrectal en la resonancia magnética de referencia para la evaluación del riesgo local y sistémico, proporcionando un programa personalizado de tratamiento neoadyuvante. Consulte Video Resumen en http://links.lww.com/DCR/B682. (Traducción- Dr. Francisco M. Abarca-Rendon).

Anatomical variations of the branches from left colic artery and middle colic artery at splenic flexure.

BACKGROUND: Variations of the vasculature at splenic flexure by left colic artery (LCA) and middle colic artery (MCA) remain ambiguous. OBJECTIVES: This study aim to investigate the anatomical variations of the branches from LCA and MCA at splenic flexure area. METHODS: Using ultra-thin CT images (0.5-mm thickness), we traced LCA and MCA till their merging site with paracolic marginal arteries through maximum intensity projection (MIP) reconstruction and computed tomography angiography (3D-CTA). RESULTS: A total of 229 cases were retrospectively enrolled. LCA ascending branch approached upwards till the distal third of the transverse colon in 37.6%, reached the splenic flexure in 37.6%, and reached the lower descending colon in 23.1%, and absent in 1.7% of the cases. Areas supplied by MCA left branch and aMCA were 33.2%, 44.5% and 22.3% in the proximal, middle and distal third of transverse colon of the cases, respectively. The accessory MCA separately originated from the superior mesenteric artery was found in 17.9% of the cases. Mutual correlation was found that, when the LCA ascending branch supplied the distal transverse colon, MCA left branch tended to feed the proximal transverse colon; when the LCA ascending branch supplied the lower part of descending colon, MCA left branch was more likely to feed the distal third of transverse colon. CONCLUSIONS: Vasculature at splenic flexure by LCA and MCA varied at specific pattern. This study could add more anatomical details for vessel management in surgeries for left-sided colon cancer.

Comparison of outcomes between neoadjuvant imatinib and upfront surgery in patients with localized rectal GIST: An inverse probability of treatment weighting analysis.

BACKGROUND AND OBJECTIVES: This study aimed to compare outcomes between neoadjuvant imatinib and upfront surgery in patients with localized rectal gastrointestinal stromal tumors (GIST) patients. METHODS: Eighty-five patients with localized rectal GIST were divided into two groups: upfront surgery ± adjuvant imatinib (Group A, n = 33) and the neoadjuvant imatinib + surgery + adjuvant imatinib (Group B, n = 52). Baseline characteristics between groups were controlled for with inverse probability of treatment weighting (IPTW) adjusted analysis. RESULTS: The response rate to neoadjuvant imatinib was 65.9%. After the IPTW-adjusted analysis, patients who underwent neoadjuvant therapy had better distant recurrence-free survival (DRFS) and disease-specific survival (DSS) compared with those who underwent upfront surgery (5-year DRFS 97.8 vs. 71.9%, hazard ratio [HR], 0.15; 95% CI, 0.03-0.87; p = 0.03; 5-year DSS 100 vs. 77.1%; HR, 0.11; 95% CI, 0.01-0.92; p = 0.04). While no significant association was found between overall survival (OS) and treatment groups (p = 0.07), 5-year OS was higher for the neoadjuvant group than upfront surgery group (97.8% vs. 71.9%; HR, 0.2; 95% CI, 0.03-1.15). CONCLUSIONS: In patients with localized rectal GIST, neoadjuvant imatinib not only shrunk the tumor size but also decreased the risk of metastasis and tumor-related deaths when compared to upfront surgery and adjuvant imatinib alone.

Impact of pelvic dimensions on anastomotic leak after anterior resection for patients with rectal cancer.

AIM: The impact of pelvis on the development of anastomotic leak (AL) in rectal cancer (RC) patients who underwent anterior resection (AR) remains unclear. The aim of this study was to evaluate the impact of pelvic dimensions on the risk of AL. METHODS: A total of 1058 RC patients undergoing AR from January 2013 to January 2016 were enrolled. Pelvimetric parameters were obtained using abdominopelvic computed tomography scans. RESULTS: Univariate analyses showed that pelvic inlet, pelvic outlet, interspinous distance, and intertuberous distance were significantly associated with the risk for AL (P < 0.05). Multivariate analysis confirmed that pelvic inlet and intertuberous distance were independent risk factors for AL (P < 0.05). Significant factors from multivariate analysis were assembled into the nomogram A (without pelvic dimensions) and nomogram B (with pelvic dimensions). The area under curve (AUC) of nomogram B was 0.72 (95% CI 0.67-0.77), which was better than the AUC of nomogram A (0.69, [95% CI 0.65-0.74]), but didn't reach a statistical significance (P = 0.199). Decision curve supported that nomogram B was better than nomogram A. CONCLUSION: Pelvic dimensions, specifically pelvic inlet and intertuberous distance, seemed to be independent predictors for postoperative AL in RC patients. Pelvic inlet and intertuberous distance incorporated with preoperative radiotherapy, preoperative albumin, conversion, and tumor diameter in the nomogram might provide a clinical tool for predicting AL.

Better prognostic determination of cT3 rectal cancer through measurement of distance to mesorectal fascia: A multicenter study.

OBJECTIVE: To forward the magnetic resonance imaging (MRI) based distance between the deepest tumor invasion and mesorectal fascia (DMRF), and to explore its prognosis differentiation value in cT3 stage rectal cancer with comparison of cT3 substage. METHODS: This was a retrospective, multicenter cohort study including cT3 rectal cancer patients undergoing neoadjuvant chemoradiotherapy followed by radical surgery from January 2013 to September 2014. DMRF and cT3 substage were evaluated from baseline MRI. The cutoff of DMRF was determined by disease progression. Multivariate cox regression was used to test the prognostic values of baseline variables. RESULTS: A total of 804 patients were included, of which 226 (28.1%) developed progression. A DMRF cutoff of 7 mm was chosen. DMRF category, the clock position of the deepest position of tumor invasion (CDTI) and extramural venous invasion (EMVI) were independent predictors for disease progression, and hazard ratios (HRs) were 0.26 [95% confidence interval (95% CI), 0.13-0.56], 1.88 (95% CI, 1.33-2.65) and 1.57 (95% CI, 1.13-2.18), respectively. cT3 substage was not a predictor for disease progression. CONCLUSIONS: The measurement of DMRF value on baseline MRI can better distinguish cT3 rectal cancer prognosis rather than cT3 substage, and was recommended in clinical evaluation.

Risk factor analysis for inaccurate pre-operative MRI staging in rectal cancer.

BACKGROUND: Various tumor characteristics might lead to inaccurate local MRI-defined stage of rectal cancer and the purpose of this study was to explore the clinicopathological factors that impact on the precision pre-treatment MRI-defined stage of rectal cancer. METHODS: A retrospectively analysis was conducted in non-metastatic rectal cancer patients who received radical tumor resection without neoadjuvant treatment during 2007-2015 in the Sixth Affiliated Hospital of Sun Yat-sen University. Clinical T stage and N stage defined by pelvic enhanced MRI and pathological stage were compared and patients were subdivided into accurate-staging, over-staging and under-staging subgroups. Logistic regressions were used to explore risk factors for over-staging or under-staging. RESULTS: Five hundred fifty-one cases of patients were collected. Among them, 109 cases (19.4%) of patients were over-T-staged and 50 cases (8.9%) were under-T-staged, while 78 cases (13.9%) were over-N-staged and 75 cases (13.3%) were under-N-staged. Logistic regression suggested that pre-operative bowel obstruction was risk factor for over-T-staging (OR = 3.120, 95%CI: 1.662-5.857, P < 0.001) as well as over-N-staging (OR = 3.494, 95%CI: 1.797-6.794, P < 0.001), while mucinous adenocarcinoma was a risk factor for under-N-staging (OR = 4.049, 95%CI: 1.876-8.772, P < 0.001). Patients with larger tumor size were at lower risk for over-T-staging (OR = 0.837, 95%CI: 0.717-0.976, P = 0.024) and higher risk for over-N-staging (OR = 1.434, 95%CI: 1.223-1.680, P < 0.001). CONCLUSION: Bowel obstruction, mucinous adenocarcinoma and tumor size might have impact on the pre-operative MRI T staging or N staging of rectal cancer. Our results reminded clinicians to assess clinical stage individually in such rectal cancer patients.

Baseline and early 3D-CUBE volume reconstruction of locally advanced rectal cancer to predict tumor response after neoadjuvant chemotherapy.

PURPOSE: To explore whether volumetric measurements of 3D-CUBE sequences based on baseline and early treatment time can predict neoadjuvent chemotherapy (NCT) efficacy of locally advanced rectal cancer (LARC). MATERIAL AND METHOD: 73 patients with LARC were enrolled from February 2014 to January 2018. All patients underwent MRIs during the baseline period before NCT (BL-NCT) and the first month of NCT (FM-NCT), and tumor volume (TV) was measured using 3D-CUBE, and tumor volume reduction (TVR) and tumor volume reduction rate (TVRR) were calculated. In addition, tumor invasion depth, tumor maximal length, range of tumor involvement in the circumference of intestinal lumen and distance from inferior part of tumor to the anal verge were measured using baseline high-spatial-resolution T2-weighted MRIs. All patients were categorized into sensitive and insensitive groups based on post-surgical pathology after completion of the full courses of NCT. The receiver operating characteristic (ROC) curve was used to analyze the value of different MRI parameters in predicting efficacy of NCT. RESULTS: Statistically significant differences in TV of BL-NCT, TVR and TVRR from BL-NCT to FM-NCT were detected between sensitive and insensitive groups (P < 0.05, respectively). The areas under the curves (AUC) of ROC of TVR and TVRR in predicting efficacy of NCT (0.890 [95% CI, 0.795∼0.951], 0.839 [95% CI, 0.735∼0.915]) were significantly better than that of TV (0.660 [95% CI, 0.540∼0.767]) (P < 0.05, respectively). CONCLUSION: Reconstruction of 3D-CUBE volume in the first month of NCT is necessary, and both TVR and TVRR can be used as early predictors of NCT efficacy.

Radiomics-Based Pretherapeutic Prediction of Non-response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer.

OBJECTIVE: The aim of this study was to investigate whether pretherapeutic, multiparametric magnetic resonance imaging (MRI) radiomic features can be used for predicting non-response to neoadjuvant therapy in patients with locally advanced rectal cancer (LARC). METHODS: We retrospectively enrolled 425 patients with LARC [allocated in a 3:1 ratio to a primary (n = 318) or validation (n = 107) cohort] who received neoadjuvant therapy before surgery. All patients underwent T1-weighted, T2-weighted, diffusion-weighted, and contrast-enhanced T1-weighted MRI scans before receiving neoadjuvant therapy. We extracted 2424 radiomic features from the pretherapeutic, multiparametric MR images of each patient. The Wilcoxon rank-sum test, Spearman correlation analysis, and least absolute shrinkage and selection operator regression were successively performed for feature selection, whereupon a multiparametric MRI-based radiomic model was established by means of multivariate logistic regression analysis. This feature selection and multivariate logistic regression analysis was also performed on all single-modality MRI data to establish four single-modality radiomic models. The performance of the five radiomic models was evaluated by receiver operating characteristic (ROC) curve analysis in both cohorts. RESULTS: The multiparametric, MRI-based radiomic model based on 16 features showed good predictive performance in both the primary (p < 0.01) and validation (p < 0.05) cohorts, and performed better than all single-modality models. The area under the ROC curve of this multiparametric MRI-based radiomic model achieved a score of 0.822 (95% CI 0.752-0.891). CONCLUSIONS: We demonstrated that pretherapeutic, multiparametric MRI radiomic features have potential in predicting non-response to neoadjuvant therapy in patients with LARC.

Clinical Anastomotic Leakage After Rectal Cancer Resection Can Be Predicted by Pelvic Anatomic Features on Preoperative MRI Scans: A Secondary Analysis of a Randomized Controlled Trial.

BACKGROUND: We demonstrated previously that radiation proctitis induced by preoperative radiotherapy is a predisposing factor for clinical anastomotic leakage in patients undergoing rectal cancer resection. Quantitative measurement of radiation proctitis is needed. OBJECTIVE: This study aimed to quantitate the changes of anatomic features caused by preoperative radiotherapy for rectal cancer and evaluate its ability to predict leakage. DESIGN: It was a secondary analysis of a randomized controlled trial (NCT01211210). MRI variables were retrospectively assessed. SETTINGS: The study was conducted in the leading center of the trial, which is a tertiary GI hospital. PATIENTS: Patients undergoing preoperative chemoradiation with sphincter-preserving surgery were included. MAIN OUTCOME MEASURES: Anatomic features were measured by preradiotherapy and postradiotherapy MRI. Univariate analyses were used to identify prognostic factors. Receiver operating characteristic curves were constructed to determine the cutoff value of the changes of MRI variables in predicting leakage. RESULTS: Eighteen (14.4%) of the 125 included patients developed clinical anastomotic leakage. Baseline characteristics were comparable between leakage group and nonleakage group. Relative increments of width of presacral space, thickness of rectal wall, and distal end of sigmoid colon discriminate between the 2 groups better than random chance. Relative increments of width of presacral space was the best performing predictor, with area under the curve of 0.722, sensitivity of 66.7%, specificity of 72.0%, and positive and negative predictive value of 28.6% and 92.8%. LIMITATIONS: The study was limited by its small sample size and retrospective design. CONCLUSIONS: Increments of the width of the presacral space, thickness of rectal wall, and distal part of the sigmoid colon helps to identify individuals not at risk for clinical anastomotic leakage after rectal cancer resection. The first variable is the strongest predictor. Changes of these variables should be taken into consideration when evaluating the application of defunctioning stoma. See Video Abstract at http://links.lww.com/DCR/B23. CLINICAL TRIALS IDENTIFIER: NCT1211210. LAS FUGAS ANASTOMÓTICAS CLÍNICAS DESPUÉS DE LA RESECCIÓN DEL CÁNCER DEL RECTO PUEDEN PREDECIRSE POR LAS CARACTERÍSTICAS ANATÓMICAS PÉLVICAS EN LAS IMAGENES DE RESONANCIA MAGNÉTICA PREOPERATORIA: UN ANÁLISIS SECUNDARIO DE UN ESTUDIO CONTROLADO ALEATORIZADO:: Anteriormente demostramos que la proctitis inducida por la radiación de radioterapia preoperatoria es un factor predisponente para la fuga anastomótica clínica en pacientes sometidos a resección de cáncer rectal. Es necesaria la medición cuantitativa de la proctitis por radiación.Este estudio tuvo como objetivo cuantificar los cambios en las características anatómicas causados por la radioterapia preoperatoria para el cáncer de recto y evaluar su capacidad para predecir las fugas anastomoticas.Fue un análisis secundario de un estudio controlado aleatorio (NCT01211210). Los variables de imagines de resonancia magnetica se evaluaron retrospectivamente.Se llevó a cabo en el centro principal del estudio, que es un hospital gastrointestinal terciario.Se incluyeron pacientes sometidos a quimiorradiación preoperatoria con cirugía conservadora del esfínter.Las características anatómicas se midieron mediante imagines de resonancia magnetica previa y posterior a la radioterapia. Se utilizaron análisis univariados para identificar los factores pronósticos. Las curvas de características operativas del receptor se construyeron para determinar el valor de corte de los cambios de los variables de resonancia magnetica en la predicción de fugas.Dieciocho (14.4%) de los 125 pacientes incluidos desarrollaron fugas anastomóticas clínicas. Las características basales fueron comparables entre el grupo de fugas y el grupo de no fugas. Los incrementos relativos del ancho del espacio presacro, el grosor de la pared rectal y distal del colon sigmoide discriminan entre los dos grupos mejor que la posibilidad aleatoria. Los incrementos relativos del ancho del espacio presacro fueron el mejor pronóstico con un AUC de 0.722, sensibilidad del 66.7%, especificidad del 72.0%, valor predictivo positivo y negativo del 28.6% y 92.8%.Estaba limitado por el tamaño de muestra pequeño y el diseño retrospectivo.Los incrementos en el ancho del espacio presacro, el grosor de la pared rectal y la parte distal del colon sigmoide ayudan a identificar a las personas que no tienen riesgo de fuga anastomótica clínica después de la resección del cáncer rectal. La primera variable es el predictor más fuerte. Los cambios de estos variables deben tenerse en cuenta al evaluar la aplicación del estoma para desvio. Vea el Resumen del Video en http://links.lww.com/DCR/B23.

Bowel Dysfunction After Low Anterior Resection With Neoadjuvant Chemoradiotherapy or Chemotherapy Alone for Rectal Cancer: A Cross-Sectional Study from China.

BACKGROUND: Neoadjuvant therapy plays a vital role in the treatment of locally advanced rectal cancer but impairs bowel function after restorative surgery. Optimal decision making requires adequate information of functional outcomes. OBJECTIVE: This study aimed to assess postoperative bowel function and to identify predictors for severe dysfunction. DESIGN: The study included a cross-sectional cohort and retrospective assessments of pelvic anatomic features. SETTINGS: The study was conducted at a tertiary GI hospital in China. PATIENTS: Included patients underwent neoadjuvant chemoradiotherapy or chemotherapy without radiation and curative low anterior resection for rectal cancer between 2012 and 2014. MAIN OUTCOME MEASURES: Bowel function was assessed using the validated low anterior resection syndrome score. The thicknesses of the rectal wall, obturator internus, and levator ani were measured by preoperative MRI. RESULTS: A total of 151 eligible patients were identified, and 142 patients (94.0%) participated after a median of 19 months from surgery. Bowel dysfunction was observed in 71.1% (101/142) of patients, with 44.4% (63/142) reporting severe dysfunction. Symptoms of urgency and clustering were found to be major disturbances. Regression analysis identified preoperative long-course radiotherapy (p < 0.001) and a lower-third tumor (p = 0.002) independently associated with severe bowel dysfunction. Irradiated patients with a lower-third tumor (OR = 14.06; p < 0.001) or thickening of the rectal wall (OR = 11.09; p < 0.001) had a markedly increased risk of developing severe dysfunction. LIMITATIONS: The study was based on a limited cohort of patients and moderate follow-up after the primary surgery. CONCLUSIONS: Bowel function deteriorates frequently after low anterior resection for rectal cancer. Severe bowel dysfunction is significantly associated with preoperative long-course radiotherapy and a lower-third tumor, and the thickening of rectal wall after radiation is a strong predictor. Treatment decisions and patient consent should be implemented with raising awareness of bowel symptom burdens. See Video Abstract at http://links.lww.com/DCR/A317.

Liver acquisition with acceleration volume acquisition gadolinium-enhanced magnetic resonance combined with T2 sequences in the diagnosis of local recurrence of rectal cancer.

To investigate the efficacy of liver acquisition with acceleration volume acquisition (LAVA) gadolinium-enhanced magnetic resonance (MR) sequences and to assess its added accuracy in diagnosing local recurrence (LR) of rectal cancer with conventional T2-weighted fast spin echo (FSE) sequences. Pelvic MRI, including T2-weighted FSE sequences, gadolinium-enhanced sequences of LAVA and T1-weighted FSE with fat suppression, was performed on 225 patients with postoperative rectal cancer. Two readers evaluated the presence of LR according to "T2" (T2 sequences only), "T2 + LAVA-Gad" (LAVA and T2 imaging), and "T2 + T1-fs-Gad" (T1 fat suppression-enhanced sequence with T2 images). To evaluate diagnostic efficiency, imaging quality with LAVA and T1-fs-Gad by subjective scores and the signal intensity (SI) ratio. In the result, the SI ratio of LAVA was significantly higher than that of T1-fs-Gad (p = 0.0001). The diagnostic efficiency of "T2 + LAVA-Gad" was better than that of "T2 + T1-fs-Gad" (p = 0.0016 for Reader 1, p = 0.0001 for Reader 2) and T2 imaging only (p = 0.0001 for Reader 1; p = 0.0001 for Reader 2). Therefore, LAVA gadolinium-enhanced MR increases the accuracy of diagnosis of LR from rectal cancer and could replace conventional T1 gadolinium-enhanced sequences in the postoperative pelvic follow-up of rectal cancer.

Intravoxel Incoherent Motion Diffusion-weighted Magnetic Resonance Imaging combined with Texture Analysis in Predicting the Histological Grades of Rectal Adenocarcinoma.

PURPOSE: To evaluate the predictive value of 3.0T MRI Intravoxel Incoherent motion diffusion-weighted magnetic resonance imaging (IVIM-DWI) combined with texture analysis (TA) in the histological grade of rectal adenocarcinoma. METHODS: Seventy-one patients with rectal adenocarcinoma confirmed by pathology after surgical resection were collected retrospectively. According to pathology, they were divided into a poorly differentiated group (n=23) and a moderately differentiated group (n=48). The IVIM-DWI parameters and TA characteristics of the two groups were compared, and a prediction model was constructed by multivariate logistic regression analysis. ROC curves were plotted for each individual and combined parameter. RESULTS: There were statistically significant differences in D and D* values between the two groups (P < 0.05). The three texture parameters SmallAreaEmphasis, Median, and Maximum had statistically significant differences between groups (P = 0.01, 0.004, 0.009, respectively). The logistic regression prediction model showed that D*, the median, and the maximum value were significant independent predictors, and the AUC of the regression prediction model was 0.860, which was significantly higher than other single parameters. CONCLUSION: 3.0T MRI IVIM-DWI parameters combined with TA can provide valuable information for predicting the histological grades of rectal adenocarcinoma one week before the operation.

Construction and validation of a deep learning prognostic model based on digital pathology images of stage III colorectal cancer.

BACKGROUND: TNM staging is the main reference standard for prognostic prediction of colorectal cancer (CRC), but the prognosis heterogeneity of patients with the same stage is still large. This study aimed to classify the tumor microenvironment of patients with stage III CRC and quantify the classified tumor tissues based on deep learning to explore the prognostic value of the developed tumor risk signature (TRS). METHODS: A tissue classification model was developed to identify nine tissues (adipose, background, debris, lymphocytes, mucus, smooth muscle, normal mucosa, stroma, and tumor) in whole-slide images (WSIs) of stage III CRC patients. This model was used to extract tumor tissues from WSIs of 265 stage III CRC patients from The Cancer Genome Atlas and 70 stage III CRC patients from the Sixth Affiliated Hospital of Sun Yat-sen University. We used three different deep learning models for tumor feature extraction and applied a Cox model to establish the TRS. Survival analysis was conducted to explore the prognostic performance of TRS. RESULTS: The tissue classification model achieved 94.4 % accuracy in identifying nine tissue types. The TRS showed a Harrell's concordance index of 0.736, 0.716, and 0.711 in the internal training, internal validation, and external validation sets. Survival analysis showed that TRS had significant predictive ability (hazard ratio: 3.632, p = 0.03) for prognostic prediction. CONCLUSION: The TRS is an independent and significant prognostic factor for PFS of stage III CRC patients and it contributes to risk stratification of patients with different clinical stages.

Texture analysis of apparent diffusion coefficient maps: can it identify nonresponse to neoadjuvant chemotherapy for additional radiation therapy in rectal cancer patients?

Background: Neoadjuvant chemotherapy (NCT) alone can achieve comparable treatment outcomes to chemoradiotherapy in locally advanced rectal cancer (LARC) patients. This study aimed to investigate the value of texture analysis (TA) in apparent diffusion coefficient (ADC) maps for identifying non-responders to NCT. Methods: This retrospective study included patients with LARC after NCT, and they were categorized into nonresponse group (pTRG 3) and response group (pTRG 0-2) based on pathological tumor regression grade (pTRG). Predictive texture features were extracted from pre- and post-treatment ADC maps to construct a TA model using RandomForest. The ADC model was developed by manually measuring pre- and post-treatment ADC values and calculating their changes. Simultaneously, subjective evaluations based on magnetic resonance imaging assessment of TRG were performed by two experienced radiologists. Model performance was compared using the area under the curve (AUC) and DeLong test. Results: A total of 299 patients from two centers were divided into three cohorts: the primary cohort (center A; n = 194, with 36 non-responders and 158 responders), the internal validation cohort (center A; n = 49, with 9 non-responders) and external validation cohort (center B; n = 56, with 33 non-responders). The TA model was constructed by post_mean, mean_change, post_skewness, post_entropy, and entropy_change, which outperformed both the ADC model and subjective evaluations with an impressive AUC of 0.997 (95% confidence interval [CI], 0.975-1.000) in the primary cohort. Robust performances were observed in internal and external validation cohorts, with AUCs of 0.919 (95% CI, 0.805-0.978) and 0.938 (95% CI, 0.840-0.985), respectively. Conclusions: The TA model has the potential to serve as an imaging biomarker for identifying nonresponse to NCT in LARC patients, providing a valuable reference for these patients considering additional radiation therapy.

Deep learning for colorectal cancer detection in contrast-enhanced CT without bowel preparation: a retrospective, multicentre study.

BACKGROUND: Contrast-enhanced CT scans provide a means to detect unsuspected colorectal cancer. However, colorectal cancers in contrast-enhanced CT without bowel preparation may elude detection by radiologists. We aimed to develop a deep learning (DL) model for accurate detection of colorectal cancer, and evaluate whether it could improve the detection performance of radiologists. METHODS: We developed a DL model using a manually annotated dataset (1196 cancer vs 1034 normal). The DL model was tested using an internal test set (98 vs 115), two external test sets (202 vs 265 in 1, and 252 vs 481 in 2), and a real-world test set (53 vs 1524). We compared the detection performance of the DL model with radiologists, and evaluated its capacity to enhance radiologists' detection performance. FINDINGS: In the four test sets, the DL model had the area under the receiver operating characteristic curves (AUCs) ranging between 0.957 and 0.994. In both the internal test set and external test set 1, the DL model yielded higher accuracy than that of radiologists (97.2% vs 86.0%, p < 0.0001; 94.9% vs 85.3%, p < 0.0001), and significantly improved the accuracy of radiologists (93.4% vs 86.0%, p < 0.0001; 93.6% vs 85.3%, p < 0.0001). In the real-world test set, the DL model delivered sensitivity comparable to that of radiologists who had been informed about clinical indications for most cancer cases (94.3% vs 96.2%, p > 0.99), and it detected 2 cases that had been missed by radiologists. INTERPRETATION: The developed DL model can accurately detect colorectal cancer and improve radiologists' detection performance, showing its potential as an effective computer-aided detection tool. FUNDING: This study was supported by National Science Fund for Distinguished Young Scholars of China (No. 81925023); Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (No. U22A20345); National Natural Science Foundation of China (No. 82072090 and No. 82371954); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (No. 2022B1212010011); High-level Hospital Construction Project (No. DFJHBF202105).