Alessandro Fichera M.D.

Kono, T. and A. Fichera (2020). “Surgical Prophylaxis of Crohn Disease Recurrence: “Light at the End of The Tunnel”.” Ann Surg 272(2): 218-219.

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Management of Crohn disease (CD) has been in constant evolution over the past 4 decades. Advances in the medical field have reduced the need for primary surgery, but postoperative clinical, endoscopic, and ultimately surgical recurrence remains an unsolved problem. Disease recurrence typically starts on the mesenteric side of the bowel and it is predominantly located at the anastomotic or peri-anastomotic level especially in the small bowel and terminal ileal disease. Local peri-anastomotic factors have been studied including localized ischemia and denervation, local cytokine production, and more recently changes in the local microbiome, in part resulting from fecal stasis, increased transit time, and reduced or impaired motility at the anastomotic level. [No abstract; excerpt from article].

Allaix, M. E., F. Rebecchi and A. Fichera (2020). “ASO Author Reflections: Minimally Invasive Surgery for Colorectal Cancer: Where Do We Stand?” Ann Surg Oncol 2020 Aug 5. [Epub ahead of print.].

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The use of robotic platforms and the transanal transabdominal bottom-up approaches recently have been proposed to overcome the technical limitations of laparoscopy. Large RCTs are needed to define their role for rectal cancer patients.
In conclusion, current evidence supports the routine use of laparoscopy for colon and rectal cancer. [No abstract; excerpt from article.].

Allaix, M. E., F. Rebecchi and A. Fichera (2020). “The Landmark Series: Minimally Invasive (Laparoscopic and Robotic) Colorectal Cancer Surgery.” Ann Surg Oncol 2020 Jul 9. [Epub ahead of print.].

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Current high-quality evidence supports the routine use of the laparoscopic approach for patients with colon cancer. Laparoscopic colectomy is associated with earlier resumption of gastrointestinal function and shorter hospital stay, with no increased morbidity or mortality. Pathology and long-term oncologic outcomes are similar to those achieved with open surgery. The absolute benefits of laparoscopic resection for rectal cancer are still under evaluation. While its safety in terms of early postoperative clinical outcomes has been confirmed, two recent randomized controlled trial (RCTs) have questioned its routine use even in expert hands, since its non-inferiority has not been demonstrated when compared with the gold standard of open surgery. Furthermore, the impact of robotic technology is still unclear, since the only RCT available so far failed to demonstrate any benefits compared with standard laparoscopic rectal resection.

Yuan, Z., T. Xu, J. Cai, Y. Zhao, W. Cao, A. Fichera, X. Liu, J. Yao and H. Wang (2020). “Development and Validation of an Image-based Deep Learning Algorithm for Detection of Synchronous Peritoneal Carcinomatosis in Colorectal Cancer.” Ann Surg Jul 16. [Epub ahead of print.].

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OBJECTIVE: The aim of this study was to build a SVM classifier using ResNet-3D algorithm by artificial intelligence for prediction of synchronous PC. BACKGROUND: Adequate detection and staging of PC from CRC remain difficult. METHODS: The primary tumors in synchronous PC were delineated on preoperative contrast-enhanced computed tomography (CT) images. The features of adjacent peritoneum were extracted to build a ResNet3D + SVM classifier. The performance of ResNet3D + SVM classifier was evaluated in the test set and was compared to routine CT which was evaluated by radiologists. RESULTS: The training set consisted of 19,814 images from 54 patients with PC and 76 patients without PC. The test set consisted of 7837 images from 40 test patients. The ResNet-3D spent only 34 seconds to analyze the test images. To increase the accuracy of PC detection, we have built a SVM classifier by integrating ResNet-3D features with twelve PC-specific features (P < 0.05). The ResNet3D + SVM classifier showed accuracy of 94.11% with AUC of 0.922 (0.912-0.944), sensitivity of 93.75%, specificity of 94.44%, PPV of 93.75%, and NPV of 94.44% in the test set. The performance was superior to routine contrast-enhanced CT (AUC: 0.791). CONCLUSIONS: The ResNet3D + SVM classifier based on deep learning algorithm using ResNet-3D framework has shown great potential in prediction of synchronous PC in CRC.