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

Robotic surgery is associated with a decreased risk of circumferential resection margin positivity compared with conventional laparoscopic surgery in patients with rectal cancer undergoing mesorectal excision: A systematic review and meta-analysis

OBJECTIVE

To investigate whether robotic surgery (RS) decreases the risk of circumferential resection margin (CRM) positivity compared with conventional laparoscopic surgery (LS) in patients with rectal cancer (RC) undergoing mesorectal excision (ME).

BACKGROUND

Although it is well known that CRM positivity affects postoperative outcomes in patients with RC undergoing ME, few studies have investigated whether RS is superior to conventional LS for the risk of CRM positivity.

METHODS

We performed a comprehensive electronic search of the literature up to December 2022 to identify studies that compared the risk of CRM positivity between patients with RC undergoing robotic and conventional laparoscopic surgery. A meta-analysis was performed using random-effects models to calculate risk ratios (RRs) and 95 % confidence intervals (CIs), and heterogeneity was analyzed using I2 statistics.

RESULTS

Eighteen studies, consisting of 4 randomized controlled trials (RCTs) and 14 propensity score matching (PSM) studies, involved a total of 9203 patients with RC who underwent ME were included in this meta-analysis. The results demonstrated that RS decreased the overall risk of CRM positivity (RR, 0.82; 95 % CI, 0.73-0.92; P = 0.001; I2 = 0 %) compared with conventional LS. Results of a meta-analysis of the 4 selected RCTs also showed that RS decreased the risk of CRM positivity (RR, 0.62; 95 % CI, 0.43-0.91; P = 0.01; I2 = 0 %) compared with conventional LS.

CONCLUSIONS

This meta-analysis revealed that RS is associated with a decreased risk of CRM positivity compared with conventional LS in patients with RC undergoing ME.

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 ± 111.51 minutes, the blood loss was 123.78 ± 150.91 mL, the postoperative exhaust time was 3.67 ± 1.85 days, and the postoperative discharge time was 10.36 ± 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.

Development and validation of machine learning models and nomograms for predicting the surgical difficulty of laparoscopic resection in rectal cancer

BACKGROUND

The objective of this study is to develop and validate a machine learning (ML) prediction model for the assessment of laparoscopic total mesorectal excision (LaTME) surgery difficulty, as well as to identify independent risk factors that influence surgical difficulty. Establishing a nomogram aims to assist clinical practitioners in formulating more effective surgical plans before the procedure.

METHODS

This study included 186 patients with rectal cancer who underwent LaTME from January 2018 to December 2020. They were divided into a training cohort (n = 131) versus a validation cohort (n = 55). The difficulty of LaTME was defined based on Escal's et al. scoring criteria with modifications. We utilized Lasso regression to screen the preoperative clinical characteristic variables and intraoperative information most relevant to surgical difficulty for the development and validation of four ML models: logistic regression (LR), support vector machine (SVM), random forest (RF), and decision tree (DT). The performance of the model was assessed based on the area under the receiver operating characteristic curve(AUC), sensitivity, specificity, and accuracy. Logistic regression-based column-line plots were created to visualize the predictive model. Consistency statistics (C-statistic) and calibration curves were used to discriminate and calibrate the nomogram, respectively.

RESULTS

In the validation cohort, all four ML models demonstrate good performance: SVM AUC = 0.987, RF AUC = 0.953, LR AUC = 0.950, and DT AUC = 0.904. To enhance visual evaluation, a logistic regression-based nomogram has been established. Predictive factors included in the nomogram are body mass index (BMI), distance between the tumor to the dentate line ≤ 10 cm, radiodensity of visceral adipose tissue (VAT), area of subcutaneous adipose tissue (SAT), tumor diameter >3 cm, and comorbid hypertension.

CONCLUSION

In this study, four ML models based on intraoperative and preoperative risk factors and a nomogram based on logistic regression may be of help to surgeons in evaluating the surgical difficulty before operation and adopting appropriate responses and surgical protocols.

Impact of robotic total mesorectal excision upon pathology metrics in overweight males with low rectal cancer: a pooled analysis of 836 cases

The aim of this pooled analysis was to evaluate the impact of robotic total mesorectal excision (TME) on pathology metrics in Male Overweight patients with Low rectal cancer (MOL). This was a multicenter retrospective pooled analysis of data. Two groups were defined: MOL (Male, Overweight, Low rectal cancer) and non-MOL. Overweight was defined as BMI ≥ 25 kg/m2. Low rectal cancer was defined as cancer within 6 cm from the anal verge. The primary endpoints of this study were histopathological metrics, namely circumferential resection margin (CRM) (mm), CRM involvement rate (%), and the quality of TME. Circumferential resection margin (CRM) was involved if < 1 mm. 836 (106 MOL and 730 non-MOL) patients that underwent robotic TME by six surgeons over 3 years were compared. No significant differences in demographics and perioperative variables were found, except for operating time, distal margin, and number of lymph nodes harvested. CRM involvement rate did not significantly differ (7.5% vs. 5.5%, p = 0.395). Mean CRM was statistically significantly narrower in MOL patients (6.6 vs. 7.7 mm, p = 0.04). Quality of TME did not differ. Distance of tumor from the anal verge was the only independent predictor of CRM involvement. Robotic TME may provide optimal pathology metrics in overweight males with low rectal cancer. Although CRM was a few millimeters narrower in MOL, the values were within the range of uninvolved margins making the difference statistically significant, but not clinically. Being MOL was not a risk factor for involvement of circumferential resection margin.

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.