Israel Medical Association Journal

Background: For locally advanced rectal cancer patients a watch-and-wait strategy is an acceptable treatment option in cases of complete tumor response. Clinicians need robust methods of patient selection after neoadjuvant chemoradiation.

Objectives: To predict pathologic complete response (pCR) using computer vision. To analyze radiomic wavelet transform to predict pCR.

Methods: Neoadjuvant chemoradiation for patients with locally advanced rectal adenocarcinoma who passed computed tomography (CT)-based simulation procedures were examined. Gross tumor volume was examind on the set of CT simulation images. The volume has been analyzed using radiomics software package with wavelets feature extraction module. Statistical analysis using descriptive statistics and logistic regression was performed was used. For prediction evaluation a multilayer perceptron algorithm and Random Forest model were used.

Results: In the study 140 patients with II–III stage cancer were included. After a long course of chemoradiation and further surgery the pathology examination showed pCR in 38 (27.1%) of the patients. CT-simulation images of tumor volume were extracted with 850 parameters (119,000 total features). Logistic regression showed high value of wavelet contribution to model. A multilayer perceptron model showed high predictive importance of wavelet. We applied random forest analysis for classifying the texture and predominant features of wavelet parameters. Importance was assigned to wavelets.

Conclusions: We evaluated the feasibility of using non-diagnostic CT images as a data source for texture analysis combined with wavelets feature analysis for predicting pCR in locally advanced rectal cancer patients. The model performance showed the importance of including wavelets features in radiomics analysis.

Background: The learning curve for transition from open to laparoscopic proctectomies is difficult. Most surgeons have considerable laparoscopic experience prior to performing robotic-assisted procedures. There are data regarding the transition from open to robotic proctectomies. Minimally invasive anterior resection for rectal cancer has gained widespread popularity in recent years, especially when using a robotic platform.

Objectives: To analyze the experience to the transition from open to robotic anterior resection for rectal cancer.

Methods: We performed a retrospective analysis of a computerized database. All patients who had a robotic-assisted proctectomy between December 2016 and March 2019 were included and were compared to patients who underwent an open anterior resection in the same time period. A single experienced colorectal surgeon with no prior experience in colorectal laparoscopic surgery performed the procedures.

Results: During the study period, 55 patients underwent robotic-assisted proctectomy and 55 had an open proctectomy. Patients had similar pre-operative demographic and clinical characteristics with the majority of patients receiving neoadjuvant chemoradiation. The surgical time was significantly lower in the open surgery group (168 minutes vs. 310 minutes, P = 0.005). Both the surgical and pathological outcomes did not differ significantly between the two groups, with good short-term oncologic outcomes and low complication rates.

Conclusions: The transition from open to robotic-assisted proctectomy is feasible and safe and provides a good alternative for undertaking a minimally invasive surgery for the experienced open colorectal surgeon