Role of Perirectal Fat in the Carcinogenesis and Development of Early-Onset Rectal Cancer

Obesity and early-onset colorectal cancer risk: emerging clinical evidence and biological mechanisms

Early-onset colorectal cancer (EOCRC) is defined as diagnosed at younger than 50 years of age and indicates a health burden globally. Patients with EOCRC have distinct risk factors, clinical characteristics, and molecular pathogenesis compared with older patients with CRC. Further investigations have identified different roles of obesity between EOCRC and late-onset colorectal cancer (LOCRC). Most studies have focused on the clinical characteristics of obesity in EOCRC, therefore, the mechanism involved in the association between obesity and EOCRC remains inconclusive. This review further states that obesity affects the carcinogenesis of EOCRC as well as its development and progression, which may lead to obesity-related metabolic syndrome, intestinal dysbacteriosis, and intestinal inflammation.

Predicting tumor deposits in rectal cancer: a combined deep learning model using T2-MR imaging and clinical features

BACKGROUND

Tumor deposits (TDs) are associated with poor prognosis in rectal cancer (RC). This study aims to develop and validate a deep learning (DL) model incorporating T2-MR image and clinical factors for the preoperative prediction of TDs in RC patients.

METHODS AND METHODS

A total of 327 RC patients with pathologically confirmed TDs status from January 2016 to December 2019 were retrospectively recruited, and the T2-MR images and clinical variables were collected. Patients were randomly split into a development dataset (n = 246) and an independent testing dataset (n = 81). A single-channel DL model, a multi-channel DL model, a hybrid DL model, and a clinical model were constructed. The performance of these predictive models was assessed by using receiver operating characteristics (ROC) analysis and decision curve analysis (DCA).

RESULTS

The areas under the curves (AUCs) of the clinical, single-DL, multi-DL, and hybrid-DL models were 0.734 (95% CI, 0.674-0.788), 0.710 (95% CI, 0.649-0.766), 0.767 (95% CI, 0.710-0.819), and 0.857 (95% CI, 0.807-0.898) in the development dataset. The AUC of the hybrid-DL model was significantly higher than the single-DL and multi-DL models (both p < 0.001) in the development dataset, and the single-DL model (p = 0.028) in the testing dataset. Decision curve analysis demonstrated the hybrid-DL model had higher net benefit than other models across the majority range of threshold probabilities.

CONCLUSIONS

The proposed hybrid-DL model achieved good predictive efficacy and could be used to predict tumor deposits in rectal cancer.

CRITICAL RELEVANCE STATEMENT

The proposed hybrid-DL model achieved good predictive efficacy and could be used to predict tumor deposits in rectal cancer.

KEY POINTS

• Preoperative non-invasive identification of TDs is of great clinical significance. • The combined hybrid-DL model achieved good predictive efficacy and could be used to predict tumor deposits in rectal cancer. • A preoperative nomogram provides gastroenterologist with an accurate and effective tool.

Multiregional-based magnetic resonance imaging radiomics model for predicting tumor deposits in resectable rectal cancer

PURPOSE

To establish and validate an integrated model incorporating multiregional magnetic resonance imaging (MRI) radiomics features and clinical factors to predict tumor deposits (TDs) preoperatively in resectable rectal cancer (RC).

METHODS

This study retrospectively included 148 resectable RC patients [TDs+ (n = 45); TDs- (n = 103)] from August 2016 to August 2022, who were divided randomly into a testing cohort (n = 45) and a training cohort (n = 103). Radiomics features were extracted from the volume of interest on T2-weighted images (T2WI) and diffusion-weighted images (DWI) from pretreatment MRI. Model construction was performed after feature selection. Finally, five classification models were developed by support vector machine (SVM) algorithm to predict TDs in resectable RC using the selected clinical factor, single-regional radiomics features (extracted from primary tumor), and multiregional radiomics features (extracted from the primary tumor and mesorectal fat). Receiver-operating characteristic (ROC) curve analysis was employed to assess the discrimination performance of the five models. The AUCs of five models were compared by DeLon's test.

RESULTS

The training and testing cohorts included 31 (30.1%) and 14 (31.1%) patients with TDs, respectively. The AUCs of multiregional radiomics, single-regional radiomics, and the clinical models for predicting TDs were 0.839, 0.765, and 0.793, respectively. An integrated model incorporating multiregional radiomics features and clinical factors showed good predictive performance for predicting TDs in resectable RC (AUC, 0.931; 95% CI, 0.841-0.988), which demonstrated superiority over clinical model (P = 0.016), the single-regional radiomics model (P = 0.042), and the multiregional radiomics model (P = 0.025).

CONCLUSION

An integrated model combining multiregional MRI radiomic features and clinical factors can improve prediction performance for TDs and guide clinicians in implementing treatment plans individually for resectable RC patients.

Does the mesorectal fat area impact the histopathology metrics of the specimen in males undergoing TME for distal rectal cancer?

The aim of this study was to evaluate whether the mesorectal fat area (MFA) has an impact on the histopathology metrics of the specimen in male patients undergoing robotic total mesorectal excision (rTME) for cancer in the distal third of the rectum. Prospectively collected data of patients undergoing rTME for resectable rectal cancer by five surgeons during 3 years were extracted from the REgistry of Robotic SURgery for RECTal cancer (RESURRECT). MFA was measured at preoperative MRI. Distal rectal cancer was defined as within 6 cm from the anal verge. Specimen metrics included circumferential resection margin (CRM) measured by pathologists as involved if < 1 mm, distal resection margin (DRM) and TME quality. Of 890 patients who underwent rTME for rectal cancer, a subgroup analysis compared 116/581 (33.4%) with MFA > 20 cm² to 231/581 (66.6%) with MFA ≤ 20 cm². The mean CRM in patients with MFA > 20 cm² was neither statistically nor clinically significantly different from patients with MFA ≤ 20 m² (6.8 ± 5.6 mm vs. 6.0 ± 7.5 mm; p = 0.544). The quality of TME did not significantly differ: complete TME 84.3% vs. 80.3%; nearly complete TME 12.9% vs. 10.1%; incomplete TME 6.8% vs. 5.6%. The DRM was not significantly different: 1.9 ± 1.9 cm vs. 1.9 ± 2.5 cm; p = 0.847. In addition, the intraoperative complication rate was not significantly different: 4.3% (n = 5) vs. 2.2% (n = 5) (p = 0.314). This prospective multicenter study did not find any evidence to support that larger MFA would result in poorer histopathology metrics of the specimen when performing rTME in male patients with distal rectal cancer.

Novel multiparametric MRI-based radiomics in preoperative prediction of perirectal fat invasion in rectal cancer

OBJECTIVES

To investigate the feasibility and efficacy of a nomogram that combines clinical and radiomic features of magnetic resonance imaging (MRI) for preoperative perirectal fat invasion (PFI) prediction in rectal cancer.

METHODS

This was a retrospective study. A total of 363 patients from two centers were included in the study. Patients in the first center were randomly divided into training cohort (n = 212) and internal validation cohort (n = 91) at the ratio of 7:3. Patients in the second center were allocated to the external validation cohort (n = 60). Among the training cohort, the numbers of patients who were PFI positive and PFI negative were 108 and 104, respectively. The radiomics features of preoperative T₂-weighted images, diffusion-weighted images and enhanced T₁-weighted images were extracted, and the total Radscore of each patient was obtained. We created Clinic model and Radscore model, respectively, according to clinical data or Radscore only. And that, we assembled the combined model using the clinical data and Radscore. We used DeLong's test, receiver operating characteristic, calibration and decision curve analysis to assess the models' performance.

RESULTS

The three models had good performance. Clinic model and Radscore model showed equivalent performance with AUCs of 0.85, 0.82 (accuracy of 81%, 81%) in the training cohort, AUCs of 0.78, 0.86 (accuracy of 74%, 84%) in the internal cohort, and 0.84, 0.84 (accuracy of 80%, 82%) in the external cohort without statistical difference (DeLong's test, p > 0.05). AUCs and accuracy of Combined model were 0.89 and 87%, 0.90 and 88%, and 0.90 and 88% in the three cohorts, respectively, which were higher than that of Clinic model and Radscore model, but only in the training cohort with a statistical difference (DeLong's test, p < 0.05). The calibration curves of the nomogram exhibited acceptable consistency, and the decision curve analysis indicated higher net benefit in clinical practice.

CONCLUSION

A nomogram combining clinical and radiomic features of MRI to compute the probability of PFI in rectal cancer was developed and validated. It has the potential to serve as a preoperative biomarker for predicting pathological PFI of rectal cancer.

Does the mesorectal fat area impact the histopathology metrics of the specimen in males undergoing TME for distal rectal cancer?

The aim of this study was to evaluate whether the mesorectal fat area (MFA) has an impact on the histopathology metrics of the specimen in male patients undergoing robotic total mesorectal excision (rTME) for cancer in the distal third of the rectum. Prospectively collected data of patients undergoing rTME for resectable rectal cancer by five surgeons during 3 years were extracted from the REgistry of Robotic SURgery for RECTal cancer (RESURRECT). MFA was measured at preoperative MRI. Distal rectal cancer was defined as within 6 cm from the anal verge. Specimen metrics included circumferential resection margin (CRM) measured by pathologists as involved if < 1 mm, distal resection margin (DRM) and TME quality. Of 890 patients who underwent rTME for rectal cancer, a subgroup analysis compared 116/581 (33.4%) with MFA > 20 cm² to 231/581 (66.6%) with MFA ≤ 20 cm². The mean CRM in patients with MFA > 20 cm² was neither statistically nor clinically significantly different from patients with MFA ≤ 20 m² (6.8 ± 5.6 mm vs. 6.0 ± 7.5 mm; p = 0.544). The quality of TME did not significantly differ: complete TME 84.3% vs. 80.3%; nearly complete TME 12.9% vs. 10.1%; incomplete TME 6.8% vs. 5.6%. The DRM was not significantly different: 1.9 ± 1.9 cm vs. 1.9 ± 2.5 cm; p = 0.847. In addition, the intraoperative complication rate was not significantly different: 4.3% (n = 5) vs. 2.2% (n = 5) (p = 0.314). This prospective multicenter study did not find any evidence to support that larger MFA would result in poorer histopathology metrics of the specimen when performing rTME in male patients with distal rectal cancer.