A computed tomography-based multitask deep learning model for predicting tumour stroma ratio and treatment outcomes in patients with colorectal cancer: a multicentre cohort study.

BACKGROUND: Tumour-stroma interactions, as indicated by tumour-stroma ratio (TSR), offer valuable prognostic stratification information. Current histological assessment of TSR is limited by tissue accessibility and spatial heterogeneity. The authors aimed to develop a multitask deep learning (MDL) model to noninvasively predict TSR and prognosis in colorectal cancer (CRC). MATERIALS AND METHODS: In this retrospective study including 2268 patients with resected CRC recruited from four centres, the authors developed an MDL model using preoperative computed tomography (CT) images for the simultaneous prediction of TSR and overall survival. Patients in the training cohort ( n =956) and internal validation cohort (IVC, n =240) were randomly selected from centre I. Patients in the external validation cohort 1 (EVC1, n =509), EVC2 ( n =203), and EVC3 ( n =360) were recruited from other three centres. Model performance was evaluated with respect to discrimination and calibration. Furthermore, the authors evaluated whether the model could predict the benefit from adjuvant chemotherapy. RESULTS: The MDL model demonstrated strong TSR discrimination, yielding areas under the receiver operating curves (AUCs) of 0.855 (95% CI, 0.800-0.910), 0.838 (95% CI, 0.802-0.874), and 0.857 (95% CI, 0.804-0.909) in the three validation cohorts, respectively. The MDL model was also able to predict overall survival and disease-free survival across all cohorts. In multivariable Cox analysis, the MDL score (MDLS) remained an independent prognostic factor after adjusting for clinicopathological variables (all P <0.05). For stage II and stage III disease, patients with a high MDLS benefited from adjuvant chemotherapy [hazard ratio (HR) 0.391 (95% CI, 0.230-0.666), P =0.0003; HR=0.467 (95% CI, 0.331-0.659), P <0.0001, respectively], whereas those with a low MDLS did not. CONCLUSION: The multitask DL model based on preoperative CT images effectively predicted TSR status and survival in CRC patients, offering valuable guidance for personalized treatment. Prospective studies are needed to confirm its potential to select patients who might benefit from chemotherapy.

Residual abnormalities on CTE predict adverse outcomes in Crohn's disease with endoscopic healing.

BACKGROUND: Residual abnormalities on computed tomography enterography (CTE) in Crohn's disease (CD) with endoscopic healing (EH) may have prognostic implications and affect therapeutic strategy. METHODS: CD patients with EH who underwent CTE between March 2015 and June 2022 were enrolled. CTE findings of the terminal ileum and the most severe segment of colon at the time of EH were assessed respectively for each patient. Cox regression analysis and Kaplan-Meier curves were used to evaluate the association between residual abnormalities and adverse outcomes. RESULTS: A total of 140 patients (217 digestive segments) were included. Mesenteric edema (hazard ratio [HR] = 3.61, 95% CI = 1.81-7.20, P<0.001), fibrofatty proliferation (HR = 3.40, 95% CI = 1.97-5.85, P<0.001) and active small bowel inflammation (HR = 2.74, 95% CI = 1.59-4.71, P<0.001) were risk factors for clinical relapse. Furthermore, we built a scoring system using the three parameters. Radiologic score ≥ 1 was the best threshold to predict clinical relapse (HR = 4.56, 95% CI = 2.54-8.19, P<0.001) and it was validated in different outcomes. CONCLUSION: The scoring system based on three residual abnormalities on CTE can predict adverse outcomes in CD patients with EH.

Mesenteric creeping fat index defined by CT enterography is associated with early postoperative recurrence in patients with Crohn's disease.

OBJECTIVES: To investigate the value of mesenteric creeping fat index (MCFI) defined by computed-tomography enterography (CTE) in patients with Crohn's Disease (CD) for predicting early postoperative recurrence. METHODS: A total of 110 patients with CD who underwent CTE and I-stage intestinal resection surgery from December 2013 to December 2018 were enrolled. Two radiologists independently assessed CTE parameters, including MCFI, with scores ranging from 1 to 8; bowel-wall thickening, with a scale of 1 to 3; mural hyperenhancement, mural stratification, submucosal fat deposition, mesenteric fibrofatty proliferation, mesenteric hypervascularity, mesenteric fat stranding, with a scale of 0 to 2; abscess/fistula, enlarged mesenteric lymph node, abdominal and pelvic effusion, with a scale of 0 to 1. Imaging findings associated with early recurrence were assessed using logistic regression analysis. RESULTS: Within one year follow-up, early postoperative recurrence occurred in 56.4 % (62/110) patients with CD. In univariate analysis, MCFI, bowel-wall thickening, mesenteric hypervascularity, mesenteric fat stranding, abscess/fistula and mesenteric lymphadenopathy were associated with early postoperative recurrence. Among all variables, MCFI (score ≥ 4) contributes the optimal AUC (0.838 [0.758-0.919]), specificity (89.6 %), positive predictive value (90.7 %), accuracy (83.6 %), and risk ratio (OR = 32.42 [10.69-98.33], p < 0.001). In multivariate analysis, only MCFI was an independent predictor of early postoperative recurrence (OR = 25.71 [7.65-86.35], p < 0.001). CONCLUSION: CTE features are useful in predicting early postoperative recurrence in patients with CD, MCFI may be a valuable tool for clinical monitoring and follow-up.

Corrigendum: Association of CT-based delta radiomics biomarker with progression-free survival in patients with colorectal liver metastases undergo chemotherapy.

[This corrects the article DOI: 10.3389/fonc.2022.843991.].

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 ).

An MRI Deep Learning Model Predicts Outcome in Rectal Cancer.

Background Deep learning (DL) models can potentially improve prognostication of rectal cancer but have not been systematically assessed. Purpose To develop and validate an MRI DL model for predicting survival in patients with rectal cancer based on segmented tumor volumes from pretreatment T2-weighted MRI scans. Materials and Methods DL models were trained and validated on retrospectively collected MRI scans of patients with rectal cancer diagnosed between August 2003 and April 2021 at two centers. Patients were excluded from the study if there were concurrent malignant neoplasms, prior anticancer treatment, incomplete course of neoadjuvant therapy, or no radical surgery performed. The Harrell C-index was used to determine the best model, which was applied to internal and external test sets. Patients were stratified into high- and low-risk groups based on a fixed cutoff calculated in the training set. A multimodal model was also assessed, which used DL model-computed risk score and pretreatment carcinoembryonic antigen level as input. Results The training set included 507 patients (median age, 56 years [IQR, 46-64 years]; 355 men). In the validation set (n = 218; median age, 55 years [IQR, 47-63 years]; 144 men), the best algorithm reached a C-index of 0.82 for overall survival. The best model reached hazard ratios of 3.0 (95% CI: 1.0, 9.0) in the high-risk group in the internal test set (n = 112; median age, 60 years [IQR, 52-70 years]; 76 men) and 2.3 (95% CI: 1.0, 5.4) in the external test set (n = 58; median age, 57 years [IQR, 50-67 years]; 38 men). The multimodal model further improved the performance, with a C-index of 0.86 and 0.67 for the validation and external test set, respectively. Conclusion A DL model based on preoperative MRI was able to predict survival of patients with rectal cancer. The model could be used as a preoperative risk stratification tool. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Langs in this issue.

Computed tomography-based radiomics nomogram using machine learning for predicting 1-year surgical risk after diagnosis of Crohn's disease.

BACKGROUND: Identifying patients with aggressive Crohn's disease (CD) threatened by a high risk of early onset surgery is challenging. PURPOSE: We aimed to establish and validate a radiomics nomogram to predict 1-year surgical risk after the diagnosis of CD, thereby facilitating therapeutic strategies making. METHODS: Patients with CD who had undergone baseline computed tomography enterography (CTE) examination at diagnosis were recruited and randomly divided into training and test cohorts at a ratio of 7:3. Enteric phase CTE images were obtained. Inflamed segments and mesenteric fat were semiautomatically segmented, followed by feature selection and signature building. A nomogram of radiomics was constructed and validated using a multivariate logistic regression algorithm. RESULTS: A total of 268 eligible patients were retrospectively included, 69 of whom underwent surgery 1-year after diagnosis. A total of 1218 features from inflamed segments and 1218 features from peripheral mesenteric fat were extracted, and reduced to 10 and 15 potential predictors, respectively, to construct two radiomic signatures. By incorporating the radiomics signatures and clinical factors, the radiomics-clinical nomogram showed favorable calibration and discrimination in the training cohort, with an area under the curve (AUC) of 0.957, which was confirmed in the test set (AUC, 0.898). Decision curve analysis and net reclassification improvement index demonstrated the clinical usefulness of the nomogram. CONCLUSIONS: We successfully established and validated a CTE-based radiomic nomogram with both inflamed segment and mesenteric fat simultaneously evaluated to predict 1-year surgical risk in CD patients, which assisted in clinical decision-making and individualized management.

Prevalent Pseudoprogression and Pseudoresidue in Patients With Rectal Cancer Treated With Neoadjuvant Immune Checkpoint Inhibitors.

BACKGROUND: Immune checkpoint inhibitor (ICI) treatment in patients with microsatellite instability-high/mismatch repair deficient (MSI-H/dMMR) tumors holds promise in reshaping organ preservation in rectal cancer. However, the benefits are accompanied by distinctive patterns of response, introducing a dilemma in the response evaluation for clinical decision-making. PATIENTS AND METHODS: Patients with locally advanced rectal cancer with MSI-H/dMMR tumors receiving neoadjuvant ICI (nICI) treatment (n=13) and matched patients receiving neoadjuvant chemoradiotherapy (nCRT; n=13) were included to compare clinical response and histopathologic features. RESULTS: Among the 13 patients receiving nICI treatment, in the final radiologic evaluation prior to surgery (at a median of 103 days after initiation of therapy), progressive disease (n=3), stable disease (n=1), partial response (n=7), and complete response (n=2) were observed. However, these patients were later confirmed as having pathologic complete response, resulting in pseudoprogression and pseudoresidue with incidences of 23.1% (n=3) and 76.9% (n=10), respectively, whereas no pseudoprogression was found in the 13 patients receiving nCRT. We further revealed the histopathologic basis underlying the pseudoprogression and pseudoresidue by discovering the distinctive immune-related regression features after nICI treatment, including fibrogenesis, dense lymphocytes, and plasma cell infiltration. CONCLUSIONS: Pseudoprogression and pseudoresidue were unique and prevalent response patterns in MSI-H/dMMR rectal cancer after nICI treatment. Our findings highlight the importance of developing specific strategies for response evaluation in neoadjuvant immunotherapy to identify patients with a good response in whom sphincter/organ-preserving or watch-and-wait strategies may be considered.

Segmentation only uses sparse annotations: Unified weakly and semi-supervised learning in medical images.

Since segmentation labeling is usually time-consuming and annotating medical images requires professional expertise, it is laborious to obtain a large-scale, high-quality annotated segmentation dataset. We propose a novel weakly- and semi-supervised framework named SOUSA (Segmentation Only Uses Sparse Annotations), aiming at learning from a small set of sparse annotated data and a large amount of unlabeled data. The proposed framework contains a teacher model and a student model. The student model is weakly supervised by scribbles and a Geodesic distance map derived from scribbles. Meanwhile, a large amount of unlabeled data with various perturbations are fed to student and teacher models. The consistency of their output predictions is imposed by Mean Square Error (MSE) loss and a carefully designed Multi-angle Projection Reconstruction (MPR) loss. Extensive experiments are conducted to demonstrate the robustness and generalization ability of our proposed method. Results show that our method outperforms weakly- and semi-supervised state-of-the-art methods on multiple datasets. Furthermore, our method achieves a competitive performance with some fully supervised methods with dense annotation when the size of the dataset is limited.

Association of CT-Based Delta Radiomics Biomarker With Progression-Free Survival in Patients With Colorectal Liver Metastases Undergo Chemotherapy.

Predicting the prognosis of patients in advance is conducive to providing personalized treatment for patients. Our aim was to predict the therapeutic efficacy and progression free survival (PFS) of patients with liver metastasis of colorectal cancer according to the changes of computed tomography (CT) radiomics before and after chemotherapy. Methods: This retrospective study included 139 patients (397 lesions) with colorectal liver metastases who underwent neoadjuvant chemotherapy from April 2015 to April 2020. We divided the lesions into training cohort and testing cohort with a ratio of 7:3. Two - dimensional region of interest (ROI) was obtained by manually delineating the largest layers of each metastasis lesion. The expanded ROI (3 mm and 5 mm) were also included in the study to characterize microenvironment around tumor. For each of the ROI, 1,316 radiomics features were extracted from delineated plain scan, arterial, and venous phase CT images before and after neoadjuvant chemotherapy. Delta radiomics features were constructed by subtracting the radiomics features after treatment from the radiomics features before treatment. Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression were applied in the training cohort to select the valuable features. Based on clinical characteristics and radiomics features, 7 Cox proportional-hazards model were constructed to predict the PFS of patients. C-index value and Kaplan Meier (KM) analysis were used to evaluate the efficacy of predicting PFS of these models. Moreover, the prediction performance of one-year PFS was also evaluated by area under the curve (AUC). Results: Compared with the PreRad (Radiomics form pre-treatment CT images; C-index [95% confidence interval (CI)] in testing cohort: 0.614(0.552-0.675) and PostRad models (Radiomics form post-treatment CT images; 0.642(0.578-0.707), the delta model has better PFS prediction performance (Delta radiomics; 0.688(0.627-0.749). By incorporating clinical characteristics, CombDeltaRad obtains the best performance in both training cohort [C-index (95% CI): 0.802(0.772-0.832)] and the testing cohort (0.744(0.686-0.803). For 1-year PFS prediction, CombDeltaRad model obtained the best performance with AUC (95% CI) of 0.871(0.828-0.914) and 0.745 (0.651-0.838) in training cohort and testing cohort, respectively. Conclusion: CT radiomics features have the potential to predict PFS in patients with colorectal cancer and liver metastasis who undergo neoadjuvant chemotherapy. By combining pre-treatment radiomics features, post-treatment radiomics features, and clinical characteristics better prediction results can be achieved.

Mild phototherapy mediated by manganese dioxide-loaded mesoporous polydopamine enhances immunotherapy against colorectal cancer.

One of the main challenges in applying the immune checkpoint blockade to treat colorectal cancer (CRC) is the immunosuppressive tumor microenvironment. Owing to its excellent cancer cell killing ability and immune activation, mild photothermal therapy (PTT) has shown bright promise to sensitize tumors to immune checkpoint inhibition through turning the immunologically "cold" tumors into "hot" ones. Herein, a mild photothermal effect-assisted theragnostic nanodrug (MnO2@MPDA-PEG NPs) is developed by incorporating MnO2 into PEGylated-mesoporous polydopamine nanoparticles (MPDA-PEG NPs). The presence of PEG endows the theragnostic nanodrug with high biostability. After accumulation in colorectal tumor, the theragnostic nanodrug responds to the tumor microenvironment, leading to the simultaneous release of Mn2+ which serves as a magnetic resonance imaging (MRI) contrast agent for tumor imaging. The released Mn2+ could also promote mild photothermal treatment-induced immune response, including the maturation of BMDC cells. In vivo antitumor studies on a CT26 model demonstrate that MnO2@MPDA-PEG NPs could be a promising dual-imaging theragnostic nanodrug to potentiate the systemic antitumor immunities.

Intestinal fibrosis classification in patients with Crohn's disease using CT enterography-based deep learning: comparisons with radiomics and radiologists.

OBJECTIVES: Accurate evaluation of bowel fibrosis in patients with Crohn's disease (CD) remains challenging. Computed tomography enterography (CTE)-based radiomics enables the assessment of bowel fibrosis; however, it has some deficiencies. We aimed to develop and validate a CTE-based deep learning model (DLM) for characterizing bowel fibrosis more efficiently. METHODS: We enrolled 312 bowel segments of 235 CD patients (median age, 33 years old) from three hospitals in this retrospective study. A training cohort and test cohort 1 were recruited from center 1, while test cohort 2 from centers 2 and 3. All patients performed CTE within 3 months before surgery. The histological fibrosis was semi-quantitatively assessed. A DLM was constructed in the training cohort based on a 3D deep convolutional neural network with 10-fold cross-validation, and external independent validation was conducted on the test cohorts. The radiomics model (RM) was developed with 4 selected radiomics features extracted from CTE images by using logistic regression. The evaluation of CTE images was performed by two radiologists. DeLong's test and a non-inferiority test were used to compare the models' performance. RESULTS: DLM distinguished none-mild from moderate-severe bowel fibrosis with an area under the receiver operator characteristic curve (AUC) of 0.828 in the training cohort and 0.811, 0.808, and 0.839 in the total test cohort, test cohorts 1 and 2, respectively. In the total test cohort, DLM achieved better performance than two radiologists (*1 AUC = 0.579, *2 AUC = 0.646; both p < 0.05) and was not inferior to RM (AUC = 0.813, p < 0.05). The total processing time for DLM was much shorter than that of RM (p < 0.001). CONCLUSION: DLM is better than radiologists in diagnosing intestinal fibrosis on CTE in patients with CD and not inferior to RM; furthermore, it is more time-saving compared to RM. KEY POINTS: • Question Could computed tomography enterography (CTE)-based deep learning model (DLM) accurately distinguish intestinal fibrosis severity in patients with Crohn's disease (CD)? • Findings In this cross-sectional study that included 235 patients with CD, DLM achieved better performance than that of two radiologists' interpretation and was not inferior to RM with significant differences and much shorter processing time. • Meaning This DLM may accurately distinguish the degree of intestinal fibrosis in patients with CD and guide gastroenterologists to formulate individualized treatment strategies for those with bowel strictures.

A new magnetic resonance imaging tumour response grading scheme for locally advanced rectal cancer.

BACKGROUND: The potential of using magnetic resonance image tumour-regression grading (MRI-TRG) system to predict pathological TRG is debatable for locally advanced rectal cancer treated by neoadjuvant radiochemotherapy. METHODS: Referring to the American Joint Committee on Cancer/College of American Pathologists (AJCC/CAP) TRG classification scheme, a new four-category MRI-TRG system based on the volumetric analysis of the residual tumour and radiochemotherapy induced anorectal fibrosis was established. The agreement between them was evaluated by Kendall's tau-b test, while Kaplan-Meier analysis was used to calculate survival outcomes. RESULTS: In total, 1033 patients were included. Good agreement between MRI-TRG and AJCC/CAP TRG classifications was observed (k = 0.671). Particularly, as compared with other pairs, MRI-TRG 0 displayed the highest sensitivity [90.1% (95% CI: 84.3-93.9)] and specificity [92.8% (95% CI: 90.4-94.7)] in identifying AJCC/CAP TRG 0 category patients. Except for the survival ratios that were comparable between the MRI-TRG 0 and MRI-TRG 1 categories, any two of the four categories had distinguished 3-year prognosis (all P < 0.05). Cox regression analysis further proved that the MRI-TRG system was an independent prognostic factor (all P < 0.05). CONCLUSION: The new MRI-TRG system might be a surrogate for AJCC/CAP TRG classification scheme. Importantly, the system is a reliable and non-invasive way to identify patients with complete pathological responses.

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.

A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study.

Background: Accurate prediction of treatment response to neoadjuvant chemotherapy (NACT) in individual patients with locally advanced gastric cancer (LAGC) is essential for personalized medicine. We aimed to develop and validate a deep learning radiomics nomogram (DLRN) based on pretreatment contrast-enhanced computed tomography (CT) images and clinical features to predict the response to NACT in patients with LAGC. Methods: 719 patients with LAGC were retrospectively recruited from four Chinese hospitals between Dec 1st, 2014 and Nov 30th, 2020. The training cohort and internal validation cohort (IVC), comprising 243 and 103 patients, respectively, were randomly selected from center I; the external validation cohort1 (EVC1) comprised 207 patients from center II; and EVC2 comprised 166 patients from another two hospitals. Two imaging signatures, reflecting the phenotypes of the deep learning and handcrafted radiomics features, were constructed from the pretreatment portal venous-phase CT images. A four-step procedure, including reproducibility evaluation, the univariable analysis, the LASSO method, and the multivariable logistic regression analysis, was applied for feature selection and signature building. The integrated DLRN was then developed for the added value of the imaging signatures to independent clinicopathological factors for predicting the response to NACT. The prediction performance was assessed with respect to discrimination, calibration, and clinical usefulness. Kaplan-Meier survival curves based on the DLRN were used to estimate the disease-free survival (DFS) in the follow-up cohort (n = 300). Findings: The DLRN showed satisfactory discrimination of good response to NACT and yielded the areas under the receiver operating curve (AUCs) of 0.829 (95% CI, 0.739-0.920), 0.804 (95% CI, 0.732-0.877), and 0.827 (95% CI, 0.755-0.900) in the internal and two external validation cohorts, respectively, with good calibration in all cohorts (p > 0.05). Furthermore, the DLRN performed significantly better than the clinical model (p < 0.001). Decision curve analysis confirmed that the DLRN was clinically useful. Besides, DLRN was significantly associated with the DFS of patients with LAGC (p < 0.05). Interpretation: A deep learning-based radiomics nomogram exhibited a promising performance for predicting therapeutic response and clinical outcomes in patients with LAGC, which could provide valuable information for individualized treatment.

GSH-Responsive Metal-Organic Framework for Intratumoral Release of NO and IDO Inhibitor to Enhance Antitumor Immunotherapy.

Immunotherapy brings great benefits for tumor therapy in clinical treatments but encounters the severe challenge of low response rate mainly because of the immunosuppressive tumor microenvironment. Multifunctional nanoplatforms integrating effective drug delivery and medical imaging offer tremendous potential for cancer treatment, which may play a critical role in combinational immunotherapy to overcome the immunosuppressive microenvironment for efficient tumor therapy. Here, a nanodrug (BMS-SNAP-MOF) is prepared using glutathione (GSH)-sensitive metal-organic framework (MOF) to encapsulate an immunosuppressive enzyme indoleamine 2,3-dioxygenase (IDO) inhibitor BMS-986205, and the nitric oxide (NO) donor s-nitrosothiol groups. The high T1 relaxivity allows magnetic resonance imaging to monitor nanodrug distribution in vivo. After the nanodrug accumulation in tumor tissue via the EPR effect and subsequent internalization into tumor cells, the enriched GSH therein triggers cascade reactions with MOF, which disassembles the nanodrug to rapidly release the IDO-inhibitory BMS-986205 and produces abundant NO. Consequently, the IDO inhibitor and NO synergistically modulate the immunosuppressive tumor microenvironment with increase CD8+ T cells and reduce Treg cells to result in highly effective immunotherapy. In an animal study, treatment using this theranostic nanodrug achieves obvious regressions of both primary and distant 4T1 tumors, highlighting its application potential in advanced tumor immunotherapy.

Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study.

BACKGROUND: Accurate prediction of tumour response to neoadjuvant chemoradiotherapy enables personalised perioperative therapy for locally advanced rectal cancer. We aimed to develop and validate an artificial intelligence radiopathomics integrated model to predict pathological complete response in patients with locally advanced rectal cancer using pretreatment MRI and haematoxylin and eosin (H&E)-stained biopsy slides. METHODS: In this multicentre observational study, eligible participants who had undergone neoadjuvant chemoradiotherapy followed by radical surgery were recruited, with their pretreatment pelvic MRI (T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging) and whole slide images of H&E-stained biopsy sections collected for annotation and feature extraction. The RAdioPathomics Integrated preDiction System (RAPIDS) was constructed by machine learning on the basis of three feature sets associated with pathological complete response: radiomics MRI features, pathomics nucleus features, and pathomics microenvironment features from a retrospective training cohort. The accuracy of RAPIDS for the prediction of pathological complete response in locally advanced rectal cancer was verified in two retrospective external validation cohorts and further validated in a multicentre, prospective observational study (ClinicalTrials.gov, NCT04271657). Model performances were evaluated using area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). FINDINGS: Between Sept 25, 2009, and Nov 3, 2017, 303 patients were retrospectively recruited in the training cohort, 480 in validation cohort 1, and 150 in validation cohort 2; 100 eligible patients were enrolled in the prospective study between Jan 10 and June 10, 2020. RAPIDS had favourable accuracy for the prediction of pathological complete response in the training cohort (AUC 0·868 [95% CI 0·825-0·912]), and in validation cohort 1 (0·860 [0·828-0·892]) and validation cohort 2 (0·872 [0·810-0·934]). In the prospective validation study, RAPIDS had an AUC of 0·812 (95% CI 0·717-0·907), sensitivity of 0·888 (0·728-0·999), specificity of 0·740 (0·593-0·886), NPV of 0·929 (0·862-0·995), and PPV of 0·512 (0·313-0·710). RAPIDS also significantly outperformed single-modality prediction models (AUC 0·630 [0·507-0·754] for the pathomics microenvironment model, 0·716 [0·580-0·852] for the radiomics MRI model, and 0·733 [0·620-0·845] for the pathomics nucleus model; all p<0·0001). INTERPRETATION: RAPIDS was able to predict pathological complete response to neoadjuvant chemoradiotherapy based on pretreatment radiopathomics images with high accuracy and robustness and could therefore provide a novel tool to assist in individualised management of locally advanced rectal cancer. FUNDING: National Natural Science Foundation of China; Youth Innovation Promotion Association of the Chinese Academy of Sciences.

Tumor response as defined by iRECIST in gastrointestinal malignancies treated with PD-1 and PD-L1 inhibitors and correlation with survival.

BACKGROUND: Atypical tumor response patterns during immune checkpoint inhibitor therapy pose a challenge to clinicians and investigators in immuno-oncology practice. This study evaluated tumor burden dynamics to identify imaging biomarkers for treatment response and overall survival (OS) in advanced gastrointestinal malignancies treated with PD-1/PD-L1 inhibitors. METHODS: This retrospective study enrolled a total of 198 target lesions in 75 patients with advanced gastrointestinal malignancies treated with PD-1/PD-L1 inhibitors between January 2017 and March 2021. Tumor diameter changes as defined by immunotherapy Response Evaluation Criteria in Solid Tumors (iRECIST) were studied to determine treatment response and association with OS. RESULTS: Based on the best overall response, the tumor diameter ranged from - 100 to + 135.3% (median: - 9.6%). The overall response rate was 32.0% (24/75), and the rate of durable disease control for at least 6 months was 30.7% (23/75, one (iCR, immune complete response) or 20 iPR (immune partial response), or 2iSD (immune stable disease). Using univariate analysis, patients with a tumor diameter maintaining a < 20% increase (48/75, 64.0%) from baseline had longer OS than those with ≥20% increase (27/75, 36.0%) and, a reduced risk of death (median OS: 80 months vs. 48 months, HR = 0.22, P = 0.034). The differences in age (HR = 1.09, P = 0.01), combined surgery (HR = 0.15, P = 0.01) and cancer type (HR = 0.23, P = 0.001) were significant. In multivariable analysis, patients with a tumor diameter with a < 20% increase had notably reduced hazards of death (HR = 0.15, P = 0.01) after adjusting for age, combined surgery, KRAS status, cancer type, mismatch repair (MMR) status, treatment course and cancer differentiation. Two patients (2.7%) showed pseudoprogression. CONCLUSIONS: Tumor diameter with a < 20% increase from baseline during therapy in gastrointestinal malignancies was associated with therapeutic benefit and longer OS and may serve as a practical imaging marker for treatment response, clinical outcome and treatment decision making.

Radiomics diagnosed histopathological growth pattern in prediction of response and 1-year progression free survival for colorectal liver metastases patients treated with bevacizumab containing chemotherapy.

OBJECTIVE: To investigate the capability of a radiomics model, which was designed to identify histopathologic growth pattern (HGP) of colorectal liver metastases (CRLMs) based on contrast-enhanced multidetector computed tomography (ceMDCT), to predict early response and 1-year progression free survival (PFS) in patients treated with bevacizumab-containing chemotherapy. METHODS: Patients with unresectable CRLMs who were treated with bevacizumab-containing chemotherapy were included in this multicenter retrospective study. For each target lesion, the radiomics-diagnosed HGP (RAD_HGP) of desmoplastic (D) pattern or replacement (R) pattern was determined. Logistic regression and receiver operating characteristic (ROC) curves were used to assess lesion- and patient-based responses according to morphologic response criteria. One-year PFS was calculated using Kaplan-Meier curves. Hazard ratios for 1-year PFS were obtained through Cox proportional hazard regression analysis. RESULTS: Among 119 study patients, 206 D pattern and 140 R pattern lesions were identified. In patients with multiple lesions, 52 had D pattern, 31 had R pattern, and 36 had mixed (D + R) pattern. The area under the curve value for RAD_HGP in predicting early response was 0.707 for lesion-based analysis and 0.720 for patient-based analysis. Patients with D pattern had a significantly longer PFS than patients with R pattern or mixed pattern (P < 0.001). RAD_HGP was the only independent predictor of 1-year PFS. CONCLUSIONS: HGP diagnosed using a radiomics model could be used as an effective predictor of PFS for patients with CRLMs treated with bevacizumab-containing chemotherapy.

A Machine Learning Model for Predicting a Major Response to Neoadjuvant Chemotherapy in Advanced Gastric Cancer.

AIMS: To develop and validate a model for predicting major pathological response to neoadjuvant chemotherapy (NAC) in advanced gastric cancer (AGC) based on a machine learning algorithm. METHOD: A total of 221 patients who underwent NAC and radical gastrectomy between February 2013 and September 2020 were enrolled in this study. A total of 144 patients were assigned to the training cohort for model building, and 77 patients were assigned to the validation cohort. A major pathological response was defined as primary tumor regressing to ypT0 or T1. Radiomic features extracted from venous-phase computed tomography (CT) images were selected by machine learning algorithms to calculate a radscore. Together with other clinical variables selected by univariate analysis, the radscores were included in a binary logistic regression analysis to construct an integrated prediction model. The data obtained for the validation cohort were used to test the predictive accuracy of the model. RESULT: A total of 27.6% (61/221) patients achieved a major pathological response. Five features of 572 radiomic features were selected to calculate the radscores. The final established model incorporates adenocarcinoma differentiation and radscores. The model showed satisfactory predictive accuracy with a C-index of 0.763 and good fitting between the validation data and the model in the calibration curve. CONCLUSION: A prediction model incorporating adenocarcinoma differentiation and radscores was developed and validated. The model helps stratify patients according to their potential sensitivity to NAC and could serve as an individualized treatment strategy-making tool for AGC patients.