Surgeon assessment of significant rectal polyps using white light endoscopy alone and in comparison to fluorescence-augmented AI lesion classification.

PURPOSE: Perioperative decision making for large (> 2 cm) rectal polyps with ambiguous features is complex. The most common intraprocedural assessment is clinician judgement alone while radiological and endoscopic biopsy can provide periprocedural detail. Fluorescence-augmented machine learning (FA-ML) methods may optimise local treatment strategy. METHODS: Surgeons of varying grades, all performing colonoscopies independently, were asked to visually judge endoscopic videos of large benign and early-stage malignant (potentially suitable for local excision) rectal lesions on an interactive video platform (Mindstamp) with results compared with and between final pathology, radiology and a novel FA-ML classifier. Statistical analyses of data used Fleiss Multi-rater Kappa scoring, Spearman Coefficient and Frequency tables. RESULTS: Thirty-two surgeons judged 14 ambiguous polyp videos (7 benign, 7 malignant). In all cancers, initial endoscopic biopsy had yielded false-negative results. Five of each lesion type had had a pre-excision MRI with a 60% false-positive malignancy prediction in benign lesions and a 60% over-staging and 40% equivocal rate in cancers. Average clinical visual cancer judgement accuracy was 49% (with only 'fair' inter-rater agreement), many reporting uncertainty and higher reported decision confidence did not correspond to higher accuracy. This compared to 86% ML accuracy. Size was misjudged visually by a mean of 20% with polyp size underestimated in 4/6 and overestimated in 2/6. Subjective narratives regarding decision-making requested for 7/14 lesions revealed wide rationale variation between participants. CONCLUSION: Current available clinical means of ambiguous rectal lesion assessment is suboptimal with wide inter-observer variation. Fluorescence based AI augmentation may advance this field via objective, explainable ML methods.

Solitary fibrous tumor within the mesorectum: literature review based on a case report of resection by transanal minimally invasive surgery (TAMIS).

PURPOSE: Solitary fibrous tumors (SFT) are a rare entity of in majority benign neoplasms. Nevertheless, up to 20% of cases show a malignant tendency with local infiltration or metastasis. Commonly arising in the thoracic cavity, only few cases of SFT of the mesorectal tissue have been reported in the literature. Complete surgical resection, classically by posterior approach, is the treatment of choice. The purpose of this review is to demonstrate the safety and suitability of transanal minimally invasive surgery (TAMIS) as a surgical approach for the resection of benign pararectal solid tumors. METHODS: We report the case of a 52-year-old man who was diagnosed incidentally with SFT of the distal mesorectum. Resection by TAMIS was performed. Based on this case, we describe the steps and potential benefits of this procedure and provide a comprehensive review of the literature. RESULTS: Histopathology confirms the completely resected SFT. After uneventful postoperative course and discharge on day four, follow-up was recommended by a multidisciplinary board by clinical examination and MRI, which showed a well-healed scar and no recurrence up to 3 years after resection. CONCLUSION: SFT of the mesorectum is a very rare entity. To our knowledge, this is the first report on a TAMIS resection for SFT, demonstrated as a safe approach for complete resection of benign pararectal solid tumors.

Perfusion outcomes with near-infrared indocyanine green imaging system in laparoscopic total mesorectal excision for mid-rectal or low-rectal cancer (POSTER): a study protocol.

INTRODUCTION: Anastomotic leakage (AL) is defined as the failure of complete healing or disruption of the anastomosis subsequent to rectal cancer surgery, resulting in the extravasation of intestinal contents into the intra-abdominal or pelvic cavity. It is a serious complication of rectal cancer surgery, accounting for a considerable increase in morbidity and mortality. The use of fluorescence imaging technology in surgery allows surgeons to better evaluate blood perfusion. However, the conclusions of some existing studies are not consistent, so a consensus on whether the near-infrared indocyanine green (NIR-ICG) imaging system can reduce the incidence of AL is needed. METHODS: This POSTER trial is designed as a multicentre, prospective, randomised controlled clinical study adhering to the "population, interventions, comparisons, outcomes (PICO)" principles. It is scheduled to take place from August 2019 to December 2024 across eight esteemed hospitals in China. The target population consists of patients diagnosed with rectal cancer through pathological confirmation, with tumours located≤10 cm from the anal verge, eligible for laparoscopic surgery. Enrolled patients will be randomly assigned to either the intervention group or the control group. The intervention group will receive intravenous injections of ICG twice, with intraoperative assessment of anastomotic blood flow using the near-infrared NIR-ICG system during total mesorectal excision (TME) surgery. Conversely, the control group will undergo conventional TME surgery without the use of the NIR-ICG system. A 30-day follow-up period postoperation will be conducted to monitor and evaluate occurrences of AL. The primary endpoint of this study is the incidence of AL within 30 days postsurgery in both groups. The primary outcome investigators will be blinded to the application of ICG angiography. Based on prior literature, we hypothesise an AL rate of 10.3% in the control group and 3% in the experimental group for this study. With a planned ratio of 2:1 between the number of cases in the experimental and control groups, and an expected 20% lost-to-follow-up rate, the initial estimated sample size for this study is 712, comprising 474 in the experimental group and 238 in the control group. ETHICS AND DISSEMINATION: This study has been approved by Ethics committee of Beijing Friendship Hospital, Capital Medical University (approval number: 2019-P2-055-02). The results will be disseminated in major international conferences and peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT04012645.

Preoperative prediction of perineural invasion of rectal cancer based on a magnetic resonance imaging radiomics model: A dual-center study.

BACKGROUND: Perineural invasion (PNI) has been used as an important pathological indicator and independent prognostic factor for patients with rectal cancer (RC). Preoperative prediction of PNI status is helpful for individualized treatment of RC. Recently, several radiomics studies have been used to predict the PNI status in RC, demonstrating a good predictive effect, but the results lacked generalizability. The preoperative prediction of PNI status is still challenging and needs further study. AIM: To establish and validate an optimal radiomics model for predicting PNI status preoperatively in RC patients. METHODS: This retrospective study enrolled 244 postoperative patients with pathologically confirmed RC from two independent centers. The patients underwent pre-operative high-resolution magnetic resonance imaging (MRI) between May 2019 and August 2022. Quantitative radiomics features were extracted and selected from oblique axial T2-weighted imaging (T2WI) and contrast-enhanced T1WI (T1CE) sequences. The radiomics signatures were constructed using logistic regression analysis and the predictive potential of various sequences was compared (T2WI, T1CE and T2WI + T1CE fusion sequences). A clinical-radiomics (CR) model was established by combining the radiomics features and clinical risk factors. The internal and external validation groups were used to validate the proposed models. The area under the receiver operating characteristic curve (AUC), DeLong test, net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration curve, and decision curve analysis (DCA) were used to evaluate the model performance. RESULTS: Among the radiomics models, the T2WI + T1CE fusion sequences model showed the best predictive performance, in the training and internal validation groups, the AUCs of the fusion sequence model were 0.839 [95% confidence interval (CI): 0.757-0.921] and 0.787 (95%CI: 0.650-0.923), which were higher than those of the T2WI and T1CE sequence models. The CR model constructed by combining clinical risk factors had the best predictive performance. In the training and internal and external validation groups, the AUCs of the CR model were 0.889 (95%CI: 0.824-0.954), 0.889 (95%CI: 0.803-0.976) and 0.894 (95%CI: 0.814-0.974). Delong test, NRI, and IDI showed that the CR model had significant differences from other models (P < 0.05). Calibration curves demonstrated good agreement, and DCA revealed significant benefits of the CR model. CONCLUSION: The CR model based on preoperative MRI radiomics features and clinical risk factors can preoperatively predict the PNI status of RC noninvasively, which facilitates individualized treatment of RC patients.

Evaluating complete response prediction rates in locally advanced rectal cancer with different radiomics segmentation approaches.

Purpose: Studies examining prediction of complete response (CR) in locally advanced rectum cancer (LARC) from pre/post chemoradiotherapy (CRT) magnetic resonance imaging (MRI) are performed mostly with segmentations of the tumor, whereas only in two studies segmentation included tumor and mesorectum. Additionally, pelvic extramesorectal region, which is included in the clinical target volume (CTV) of radiotherapy, may contain information. Therefore, we aimed to compare predictive rates of radiomics analysis with features extracted from segmentations of tumor, tumor+mesorectum, and CTV. Methods and materials: Ninety-three LARC patients who underwent CRT in our institution between 2012 and 2019 were retrospectively scanned. Patients were divided into CR and non-CR groups. Tumor, tumor+mesorectum and CTV were segmented on T2 preCRT MRI images. Extracted features were compared for best area under the curve (AUC) of CR prediction with 15 machine-learning models. Results: CR was observed in 25 patients (26.8%), of whom 13 had pathological, and 12 had clinical complete response. For tumor, tumor+mesorectum and CTV segmentations, the best AUC were 0.84, 0.81, 0.77 in the training set and 0.85, 0.83 and 0.72 in the test set, respectively; sensitivity and specificity for the test set were 76%, 90%, 76% and 71%, 67% and 62%, respectively. Conclusion: Although the highest AUC result is obtained from the tumor segmentation, the highest accuracy and sensitivity are detected with tumor+mesorectum segmentation and these findings align with previous studies, suggesting that the mesorectum contains valuable insights for CR. The lowest result is obtained with CTV segmentation. More studies with mesorectum and pelvic nodal regions included in segmentation are needed.

Automated, High-Throughput Platform to Generate a High-Reliability, Comprehensive Rectal Cancer Database.

PURPOSE: Dynamic operations platforms allow for cross-platform data extraction, integration, and analysis, although application of these platforms to large-scale oncology enterprises has not been described. This study presents a pipeline for automated, high-fidelity extraction, integration, and validation of cross-platform oncology data in patients undergoing treatment for rectal cancer at a single, high-volume institution. METHODS: A dynamic operations platform was used to identify patients with rectal cancer treated at MD Anderson Cancer Center between 2016 and 2022 who had magnetic resonance imaging (MRI) imaging and preoperative treatment details available in the electronic health record (EHR). Demographic, clinicopathologic, tumor mutation, radiographic, and treatment data were extracted from the EHR using a methodology adaptable to any disease site. Data accuracy was assessed by manual review. Accuracy before and after implementation of synoptic reporting was determined for MRI data. RESULTS: A total of 516 patients with localized rectal cancer were included. In the era after institutional adoption of synoptic reports, the dynamic operations platform extracted T (tumor) category data from the EHR with 95% accuracy compared with 87% before the use of synoptic reports, and N (lymph node) category with 88% compared with 58%. Correct extraction of pelvic sidewall adenopathy was 94% compared with 78%, and extramural vascular invasion accuracy was 99% compared with 89%. Neoadjuvant chemotherapy and radiation data were 99% accurate for patients who had synoptic data sources. CONCLUSION: Using dynamic operations platforms enables automated cross-platform integration of multiparameter oncology data with high fidelity in patients undergoing multimodality treatment for rectal cancer. These pipelines can be adapted to other solid tumors and, together with standardized reporting, can increase efficiency in clinical research and the translation of actionable findings toward optimizing patient outcomes.

Diagnostic value of one-stop CT energy spectrum and perfusion for angiogenesis in colon and rectum cancer.

OBJECTIVE: Evaluation of the predictive value of one-stop energy spectrum and perfusion CT parameters for microvessel density (MVD) in colorectal cancer cancer foci. METHODS: Clinical and CT data of 82 patients with colorectal cancer confirmed by preoperative colonoscopy or surgical pathology in our hospital from September 2019 to November 2022 were collected and analyzed retrospectively. Energy spectrum CT images were measured using the Protocols general module of the GSI Viewer software of the GE AW 4.7 post-processing workstation to measure the CT values of the arterial and venous phase lesions and the neighboring normal intestinal wall in a single energy range of 40 kev∼140 kev, and the slopes of the energy spectrum curves (λ) were calculated between 40 kev-90 kev; Iodine concentration (IC), Water concentration (WC), Effective-Z (Eff-Z) and Normalized iodine concentration (NIC) were measured by placing a region of interest (ROI) on the iodine concentration map and water concentration map at the lesion and adjacent to the normal intestinal wall.Perfusion CT images were scanned continuously and dynamically using GSI Perfusion software and analyzed by applying CT Perfusion 4.0 software.Blood volume (BV), blood flow (BF), surface permeability (PS), time to peak (TTP), and mean transit time (MTT) were measured respectively in the lesion and adjacent normal colorectal wall. Based on the pathological findings, the tumors were divided into a low MVD group (MVD < 35/field of view, n = 52 cases) and a high MVD group (MVD ≥ 35/field of view, n = 30 cases) using a median of 35/field of view as the MVD grouping criterion. The collected data were statistically analyzed, the subjects' operating characteristic curve (ROC) was plotted, and the area under curve (AUC), sensitivity, specificity, and Yoden index were calculated for the predicted efficacy of each parameter of the energy spectrum and perfusion CT and the combined parameters. RESULTS: The CT values, IC, NIC, λ, Eff-Z of 40kev∼140kev single energy in the arterial and venous phase of colorectal cancer in the high MVD group were higher than those in the low MVD group, and the differences were all statistically significant (p < 0.05). The AUC of each single-energy CT value in the arterial phase from 40 kev to 120 kev for determining the high or low MVD of colorectal cancer was greater than 0.8, indicating that arterial stage has a good predictive value for high or low MVD in colorectal cancer; AUC for arterial IC, NIC and IC + NIC were all greater than 0.9, indicating that in arterial colorectal cancer, both single and combined parameters of spectral CT are highly effective in predicting the level of MVD. The AUC of 40 kev to 90 kev single-energy CT values in the intravenous phase was greater than 0.9, and its diagnostic efficacy was more representative; The AUC of IC and NIC in venous stage were greater than 0.8, which indicating that the IC and NIC energy spectrum parameters in venous stage colorectal cancer have a very good predictive value for the difference between high and low MVDs, with the greatest diagnostic efficacy in IC.The values of BV and BF in the high MVD group were higher than those in the low MVD group, and the differences were statistically significant (P < 0.05), and the AUC of BF, BV, and BV + BF were 0.991, 0.733, and 0.997, respectively, with the highest diagnostic efficacy for determining the level of MVD in colorectal cancer by BV + BF. CONCLUSION: One-stop CT energy spectrum and perfusion imaging technology can accurately reflect the MVD in living tumor tissues, which in turn reflects the tumor angiogenesis, and to a certain extent helps to determine the malignancy, invasion and metastasis of living colorectal cancer tumor tissues based on CT energy spectrum and perfusion parameters.

A combinatorial MRI sequence-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancer.

This study aimed to develop an optimal radiomics model for preoperatively predicting microsatellite instability (MSI) in patients with rectal cancer (RC) based on multiparametric magnetic resonance imaging. The retrospective study included 308 RC patients who did not receive preoperative antitumor therapy, among whom 51 had MSI. Radiomics features were extracted and dimensionally reduced from T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), diffusion-weighted imaging (DWI), and T1-weighted contrast enhanced (T1CE) images for each patient, and the features of each sequence were combined. Multifactor logistic regression was used to screen the optimal feature set for each combination. Different machine learning methods were applied to construct predictive MSI status models. Relative standard deviation values were determined to evaluate model performance and select the optimal model. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses were performed to evaluate model performance. The model constructed using the k-nearest neighbor (KNN) method combined with T2WI and T1CE images performed best. The area under the curve values for prediction of MSI with this model were 0.849 (0.804-0.887), with a sensitivity of 0.784 and specificity of 0.805. The Delong test showed no significant difference in diagnostic efficacy between the KNN-derived model and the traditional logistic regression model constructed using T1WI + DWI + T1CE and T2WI + T1WI + DWI + T1CE data (P > 0.05) and the diagnostic efficiency of the KNN-derived model was slightly better than that of the traditional model. From ROC curve analysis, the KNN-derived model significantly distinguished patients at low- and high-risk of MSI with the optimal threshold of 0.2, supporting the clinical applicability of the model. The model constructed using the KNN method can be applied to noninvasively predict MSI status in RC patients before surgery based on radiomics features from T2WI and T1CE images. Thus, this method may provide a convenient and practical tool for formulating treatment strategies and optimizing individual clinical decision-making for patients with RC.

MRI-based radiomics feature combined with tumor markers to predict TN staging of rectal cancer.

The aim of this study is to evaluate the predictive ability of MRI-based radiomics combined with tumor markers for TN staging in patients with rectal cancer and to develop a prediction model for TN staging. A total of 190 patients with rectal adenocarcinoma who underwent total mesorectal excision at the First Affiliated Hospital of the Air Force Medical University between January 2016 and December 2020 were included in the study. An additional 54 patients from a prospective validation cohort were included between August 2022 and August 2023. Preoperative tumor markers and MRI imaging data were collected from all enrolled patients. The 190 patients were divided into a training cohort (n = 133) and a validation cohort (n = 57). Radiomics features were extracted by outlining the region of interest (ROI) on T2WI sequence images. Feature selection and radiomics score (Rad-score) construction were performed using least absolute shrinkage and selection operator regression analysis (LASSO). The postoperative pathology TNM stage was used to differentiate locally advanced rectal cancer (T3/4 or N1/2) from locally early rectal cancer (T1/2, N0). Logistic regression was used to construct separate prediction models for T stage and N stage. The models' predictive performance was evaluated using DCA curves and calibration curves. The T staging model showed that Rad-score, based on 8 radiomics features, was an independent predictor of T staging. When combined with CEA, tumor diameter, mesoretal fascia (MRF), and extramural venous invasion (EMVI), it effectively differentiated between T1/2 and T3/4 stage rectal cancers in the training cohort (AUC 0.87 [95% CI: 0.81-0.93]). The N-staging model found that Rad-score, based on 10 radiomics features, was an independent predictor of N-staging. When combined with CA19.9, degree of differentiation, and EMVI, it effectively differentiated between N0 and N1/2 stage rectal cancers. The training cohort had an AUC of 0.84 (95% CI: 0.77-0.91). The calibration curves demonstrated good precision between the predicted and actual results. The DCA curves indicated that both sets of predictive models could provide net clinical benefits for diagnosis. MRI-based radiomics features are independent predictors of T staging and N staging. When combined with tumor markers, they have good predictive efficacy for TN staging of rectal cancer.

MRI-based radiomic models for the preoperative prediction of extramural venous invasion in rectal cancer: A systematic review and meta-analysis.

AIM: To estimate the diagnostic value of magnetic resonance imaging (MRI)-based radiomic models in detecting the extramural venous invasion (EMVI) of rectal cancer. MATERIALS AND METHODS: Appropriate studies in multiple electronic databases were systematically retrieved. The Quality Assessment of Diagnostic Accuracy Studies 2 and Radiomics Quality Score (RQS) were used to evaluate the eligible studies' methodology quality. Summary accuracy metrics were calculated, and the publication bias was detected using Deek's funnel plot. The sensitivity and meta-regression analysis were performed to investigate the causes of heterogeneity. RESULTS: For the seven eligible studies, which included 1175 patients, the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.80 (95 % CI, 0.70-0.88), 0.89 (95 % CI, 0.84-0.92), 7.0 (95 % CI, 4.7, 10.4), 0.22 (95 % CI, 0.14, 0.34), and 32 (95 % CI, 16, 65), respectively. The area under the receiver operating characteristic curve (AUC) was 0.91 (95 % CI, 0.88, 0.93). Moderate heterogeneity was found due to I2 values of 38.63 % and 32.29 % in sensitivity and specificity, respectively. Meta-regression analysis suggested that the patient enrollment, number of patients, segmentation method, and RQS score were the source of the heterogeneity. The head-to-head analysis suggested that radiomics model had a higher sensitivity for detection of EMVI than subjective evaluation by radiologist (0.47 vs. 0.73, p ≤ 0.001). CONCLUSION: Our study suggests that MRI-based radiomic models have good diagnostic value in detecting EMVI for rectal cancer patients. Nevertheless, more prospective and high-quality studies with larger sample sizes are needed in the future to validate these results.

A dynamic nomogram for predicting pathologic complete response to neoadjuvant chemotherapy in locally advanced rectal cancer.

AIM: To explore the clinical factors associated with pathologic complete response (pCR) for locally advanced rectal cancer (LARC) patients treated with neoadjuvant chemoradiotherapy (nCRT) and develop a web-based dynamic nomogram. METHODS: Retrospective analysis of patients with examination confirmed LARC from 2011 to 2022. Patients from the Union Hospital of Fujian Medical University were included as the training cohort (n = 1579) and Zhangzhou Hospital of Fujian Medical University as the external validation cohort (n = 246). RESULTS: In the training cohort, after nCRT, 350 (22.2%) patients achieved pCR. More stomas were avoided in pCR patients (73.9% vs. 69.7%, p = 0.043). After a median follow-up time of 47.7 months (IQR 2-145) shown OS (5-year: 93.7% vs. 81.0%, HR = 0.310, 95%CI: 0.189-0.510, p < 0.001) and DFS (5-year: 91.2% vs. 75.0%, HR = 0.204, 95%CI: 0.216-0.484, p < 0.001) were significantly better among patients with pCR than non-pCR. Multivariable Logistic analysis shown pCR was significantly associated with Pre-CRT CEA (HR = 0.944, 95%CI: 0.921-0.968; p < 0.001), histopathology (HR = 4.608, 95%CI: 2.625-8.089; p < 0.001), Pre-CRT T stage (HR = 0.793, 95%CI: 0.634-0.993; p = 0.043), Pre-CRT N stage (HR = 0.727, 95%CI: 0.606-0.873; p = 0.001), Pre-CRT MRI EMVI (HR = 0.352, 95%CI: 0.262-0.473; p < 0.001), total neoadjuvant therapy (HR = 2.264, 95%CI: 1.280-4.004; p = 0.005). Meanwhile, the online version of the nomogram established in this study was publicized on an open-access website (URL: https://pcrpredict.shinyapps.io/LARC2/). The model predicted accuracy with a C-index of 0.73 (95% CI: 0.70-0.75), with an average C-index of 0.73 for the internal cross validation and 0.78 (95% CI: 0.72-0.83) for the external validation cohort, showing excellent model accuracy. Delong test results showed the model has an important gain value for clinical characteristics to predict pCR in rectal cancer. CONCLUSIONS: Patients with pCR had a better prognosis, including OS and DFS, and were independently associated with Pre-CRT CEA, histopathology, Pre-CRT T/N stage, Pre-CRT MRI EMVI, and TNT. A web-based dynamic nomogram was successfully established for clinical use at any time.

Total mesorectal excision in MRI-defined low rectal cancer: multicentre study comparing oncological outcomes of robotic, laparoscopic and transanal total mesorectal excision in high-volume centres.

BACKGROUND: The routine use of MRI in rectal cancer treatment allows the use of a strict definition for low rectal cancer. This study aimed to compare minimally invasive total mesorectal excision in MRI-defined low rectal cancer in expert laparoscopic, transanal and robotic high-volume centres. METHODS: All MRI-defined low rectal cancer operated on between 2015 and 2017 in 11 Dutch centres were included. Primary outcomes were: R1 rate, total mesorectal excision quality and 3-year local recurrence and survivals (overall and disease free). Secondary outcomes included conversion rate, complications and whether there was a perioperative change in the preoperative treatment plan. RESULTS: Of 1071 eligible rectal cancers, 633 patients with low rectal cancer were identified. Quality of the total mesorectal excision specimen (P = 0.337), R1 rate (P = 0.107), conversion (P = 0.344), anastomotic leakage rate (P = 0.942), local recurrence (P = 0.809), overall survival (P = 0.436) and disease-free survival (P = 0.347) were comparable among the centres. The laparoscopic centre group had the highest rate of perioperative change in the preoperative treatment plan (10.4%), compared with robotic expert centres (5.2%) and transanal centres (2.1%), P = 0.004. The main reason for this change was stapling difficulty (43%), followed by low tumour location (29%). Multivariable analysis showed that laparoscopic surgery was the only independent risk factor for a change in the preoperative planned procedure, P = 0.024. CONCLUSION: Centres with expertise in all three minimally invasive total mesorectal excision techniques can achieve good oncological resection in the treatment of MRI-defined low rectal cancer. However, compared with robotic expert centres and transanal centres, patients treated in laparoscopic centres have an increased risk of a change in the preoperative intended procedure due to technical limitations.

Machine learning-based response assessment in patients with rectal cancer after neoadjuvant chemoradiotherapy: radiomics analysis for assessing tumor regression grade using T2-weighted magnetic resonance images.

PURPOSE: This study aimed to assess tumor regression grade (TRG) in patients with rectal cancer after neoadjuvant chemoradiotherapy (NCRT) through a machine learning-based radiomics analysis using baseline T2-weighted magnetic resonance (MR) images. MATERIALS AND METHODS: In total, 148 patients with locally advanced rectal cancer(T2-4 or N+) who underwent MR imaging at baseline and after chemoradiotherapy between January 2010 and May 2021 were included. A region of interest for each tumor mass was drawn by a radiologist on oblique axial T2-weighted images, and main features were selected using principal component analysis after dimension reduction among 116 radiomics and three clinical features. Among eight learning models that were used for prediction model development, the model showing best performance was selected. Treatment responses were classified as either good or poor based on the MR-assessed TRG (mrTRG) and pathologic TRG (pTRG). The model performance was assessed using the area under the receiver operating curve (AUROC) to classify the response group. RESULTS: Approximately 49% of the patients were in the good response (GR) group based on mrTRG (73/148) and 26.9% based on pTRG (28/104). The AUCs of clinical data, radiomics models, and combined radiomics with clinical data model for predicting mrTRG were 0.80 (95% confidence interval [CI] 0.73, 0.87), 0.74 (95% CI 0.66, 0.81), and 0.75(95% CI 0.68, 0.82), and those for predicting pTRG was 0.62 (95% CI 0.52, 0.71), 0.74 (95% CI 0.65, 0.82), and 0.79 (95% CI 0.71, 0.87). CONCLUSION: Radiomics combined with clinical data model using baseline T2-weighted MR images demonstrated feasible diagnostic performance in predicting both MR-assessed and pathologic treatment response in patients with rectal cancer after NCRT.

Determining the optimal distal resection margin in rectal cancer patients by imaging of large pathological sections: An experimental study.

OBJECTIVE: To determine the distal resection margin in sphincter-sparing surgery in patients with low rectal cancer based on imaging of large pathological sections. METHODS: Patients who underwent sphincter-sparing surgery for ultralow rectal cancer at Guangxi Medical University Cancer Hospital within the period from January 2016 to March 2022 were tracked and observed. The clinical and pathological data of the patients were collected and analyzed. The EVOS fluorescence automatic cell imaging system was used for imaging large pathological sections. Follow-up patient data were acquired mainly by sending the patients letters and contacting them via phone calls, and during outpatient visits. RESULTS: A total of 46 patients (25 males, 21 females) aged 27 to 86 years participated in the present study. Regarding clinical staging, there were 9, 10, 16, and 10 cases with stages I, II, III, and IV low rectal cancer, respectively. The surgical time was 273.82 ±â€…111.51 minutes, the blood loss was 123.78 ±â€…150.91 mL, the postoperative exhaust time was 3.67 ±â€…1.85 days, and the postoperative discharge time was 10.36 ±â€…5.41 days. There were 8 patients with complications, including 3 cases of pulmonary infection, 2 cases of intestinal obstruction, one case of pleural effusion, and one case of stoma necrosis. The longest and shortest distal resection margins (distances between the cutting edges and the tumor edges) were 3 cm and 1 cm, respectively. The minimum length of the extension areas of the tumor lesions in the 46 images of large pathological sections was 0.1 mm, and the maximum length was 15 mm. Among the tumor lesions, 91.30% (42/46) had an extension area length of ≤5 mm, and 97.83% (45/46) had an extension area length of ≤10 mm. The length of the extension zone was not related to clinical pathological parameters (P > .05). CONCLUSION: In the vast majority of cases, the distal resection margin was at least 1 cm; thus, "No Evidence of Disease" could have been achieved. Additional high-powered randomized trials are needed to confirm the results of the present study.

Construction of a nomogram model to predict technical difficulty in performing laparoscopic sphincter-preserving radical resection for rectal cancer.

BACKGROUND: Colorectal surgeons are well aware that performing surgery for rectal cancer becomes more challenging in obese patients with narrow and deep pelvic cavities. Therefore, it is essential for colorectal surgeons to have a comprehensive understanding of pelvic structure prior to surgery and anticipate potential surgical difficulties. AIM: To evaluate predictive parameters for technical challenges encountered during laparoscopic radical sphincter-preserving surgery for rectal cancer. METHODS: We retrospectively gathered data from 162 consecutive patients who underwent laparoscopic radical sphincter-preserving surgery for rectal cancer. Three-dimensional reconstruction of pelvic bone and soft tissue parameters was conducted using computed tomography (CT) scans. Operative difficulty was categorized as either high or low, and multivariate logistic regression analysis was employed to identify predictors of operative difficulty, ultimately creating a nomogram. RESULTS: Out of 162 patients, 21 (13.0%) were classified in the high surgical difficulty group, while 141 (87.0%) were in the low surgical difficulty group. Multivariate logistic regression analysis showed that the surgical approach using laparoscopic intersphincteric dissection, intraoperative preventive ostomy, and the sacrococcygeal distance were independent risk factors for highly difficult laparoscopic radical sphincter-sparing surgery for rectal cancer (P < 0.05). Conversely, the anterior-posterior diameter of pelvic inlet/sacrococcygeal distance was identified as a protective factor (P < 0.05). A nomogram was subsequently constructed, demonstrating good predictive accuracy (C-index = 0.834). CONCLUSION: The surgical approach, intraoperative preventive ostomy, the sacrococcygeal distance, and the anterior-posterior diameter of pelvic inlet/sacrococcygeal distance could help to predict the difficulty of laparoscopic radical sphincter-preserving surgery.

Imaging segmentation mechanism for rectal tumors using improved U-Net.

OBJECTIVE: In radiation therapy, cancerous region segmentation in magnetic resonance images (MRI) is a critical step. For rectal cancer, the automatic segmentation of rectal tumors from an MRI is a great challenge. There are two main shortcomings in existing deep learning-based methods that lead to incorrect segmentation: 1) there are many organs surrounding the rectum, and the shape of some organs is similar to that of rectal tumors; 2) high-level features extracted by conventional neural networks often do not contain enough high-resolution information. Therefore, an improved U-Net segmentation network based on attention mechanisms is proposed to replace the traditional U-Net network. METHODS: The overall framework of the proposed method is based on traditional U-Net. A ResNeSt module was added to extract the overall features, and a shape module was added after the encoder layer. We then combined the outputs of the shape module and the decoder to obtain the results. Moreover, the model used different types of attention mechanisms, so that the network learned information to improve segmentation accuracy. RESULTS: We validated the effectiveness of the proposed method using 3773 2D MRI datasets from 304 patients. The results showed that the proposed method achieved 0.987, 0.946, 0.897, and 0.899 for Dice, MPA, MioU, and FWIoU, respectively; these values are significantly better than those of other existing methods. CONCLUSION: Due to time savings, the proposed method can help radiologists segment rectal tumors effectively and enable them to focus on patients whose cancerous regions are difficult for the network to segment. SIGNIFICANCE: The proposed method can help doctors segment rectal tumors, thereby ensuring good diagnostic quality and accuracy.

The Additional Role of F18-FDG PET/CT in Characterizing MRI-Diagnosed Tumor Deposits in Locally Advanced Rectal Cancer.

Rationale: F18-FDG PET/CT may be helpful in baseline staging of patients with high-risk LARC presenting with vascular tumor deposits (TDs), in addition to standard pelvic MRI and CT staging. Methods: All patients with locally advanced rectal cancer that had TDs on their baseline MRI of the pelvis and had a baseline F18-FDG PET/CT between May 2016 and December 2020 were included in this retrospective study. TDs as well as lymph nodes identified on pelvic MRI were correlated to the corresponding nodular structures on a standard F18-FDG PET/CT, including measurements of nodular SUVmax and SUVmean. In addition, the effects of partial volume and spill-in on SUV measurements were studied. Results: A total number of 62 patients were included, in which 198 TDs were identified as well as 106 lymph nodes (both normal and metastatic). After ruling out partial volume effects and spill-in, 23 nodular structures remained that allowed for reliable measurement of SUVmax: 19 TDs and 4 LNs. The median SUVmax between TDs and LNs was not significantly different (p = 0.096): 4.6 (range 0.8 to 11.3) versus 2.8 (range 1.9 to 3.9). For the median SUVmean, there was a trend towards a significant difference (p = 0.08): 3.9 (range 0.7 to 7.8) versus 2.3 (range 1.5 to 3.4). Most nodular structures showing either an SUVmax or SUVmean ≥ 4 were characterized as TDs on MRI, while only two were characterized as LNs. Conclusions: SUV measurements may help in separating TDs from lymph node metastases or normal lymph nodes in patients with high-risk LARC.

Evaluation of anorectal function using real-time tissue elastography before and after preoperative chemoradiotherapy.

PURPOSE: This study aimed to clarify the relationship between changes in elasticity and anorectal function before and after chemoradiotherapy. METHODS: This is a single-center prospective cohort study (Department of Surgical Oncology, The University of Tokyo). We established a technique to quantify internal anal sphincter hardness as elasticity using transanal ultrasonography with real-time tissue elastography. Twenty-seven patients with post-chemoradiotherapy rectal cancer during 2019-2022 were included. Real-time tissue elastography with transanal ultrasonography was performed before and after chemoradiotherapy to measure internal anal sphincter hardness as "elasticity" (hardest (0) to softest (255); decreased elasticity indicated sclerotic changes). The relationship between the increase or decrease in elasticity pre- and post-chemoradiotherapy and the maximum resting pressure, maximum squeeze pressure, and Wexner score were the outcome measures. RESULTS: A decrease in elasticity was observed in 16/27 (59.3%) patients after chemoradiotherapy. Patients with and without elasticity decrease after chemoradiotherapy comprised the internal anal sphincter sclerosis and non-sclerosis groups, respectively. The maximum resting pressure post-chemoradiotherapy was significantly high in the internal anal sphincter sclerosis group (63.0 mmHg vs. 47.0 mmHg), and a majority had a worsening Wexner score (60.0% vs. 18.2%) compared with that of the non-sclerosis group. Decreasing elasticity (internal anal sphincter sclerosis) correlated with a higher maximum resting pressure (r = 0.36); no correlation was observed between the degree of elasticity change and maximum squeeze pressure. CONCLUSION: Internal anal sphincter sclerosis due to chemoradiotherapy may correlate to anorectal dysfunction.

A medical image segmentation method for rectal tumors based on multi-scale feature retention and multiple attention mechanisms.

BACKGROUND: With the continuous development of deep learning algorithms in the field of medical images, models for medical image processing based on convolutional neural networks have made great progress. Since medical images of rectal tumors are characterized by specific morphological features and complex edges that differ from natural images, achieving good segmentation results often requires a higher level of enrichment through the utilization of semantic features. PURPOSE: The efficiency of feature extraction and utilization has been improved to some extent through enhanced hardware arithmetic and deeper networks in most models. However, problems still exist with detail loss and difficulty in feature extraction, arising from the extraction of high-level semantic features in deep networks. METHODS: In this work, a novel medical image segmentation model has been proposed for Magnetic Resonance Imaging (MRI) image segmentation of rectal tumors. The model constructs a backbone architecture based on the idea of jump-connected feature fusion and solves the problems of detail feature loss and low segmentation accuracy using three novel modules: Multi-scale Feature Retention (MFR), Multi-branch Cross-channel Attention (MCA), and Coordinate Attention (CA). RESULTS: Compared with existing methods, our proposed model is able to segment the tumor region more effectively, achieving 97.4% and 94.9% in Dice and mIoU metrics, respectively, exhibiting excellent segmentation performance and computational speed. CONCLUSIONS: Our proposed model has improved the accuracy of both lesion region and tumor edge segmentation. In particular, the determination of the lesion region can help doctors identify the tumor location in clinical diagnosis, and the accurate segmentation of the tumor edge can assist doctors in judging the necessity and feasibility of surgery.