Combined endoscopic and laparoscopic surgery (CELS) for early colon cancer in high-risk patients

Background Local excision of early colon cancers could be an option in selected patients with high risk of complications and no sign of lymph node metastasis (LNM). The primary aim was to assess feasibility in high-risk patients with early colon cancer treated with Combined Endoscopic and Laparoscopic Surgery (CELS). Methods A non-randomized prospective feasibility study including 25 patients with Performance Status score ≥ 1 and/or American Society of Anesthesiologists score ≥ 3, and clinical Union of International Cancer Control stage-1 colon cancer suitable for CELS resection. The primary outcome was failure of CELS resection, defined as either: Incomplete resection (R1/R2), local recurrence within 3 months, complication related to CELS within 30 days (Clavien–Dindo grade ≥ 3), death within 30 days or death within 90 days due to complications to surgery. Results Fifteen patients with clinical T1 (cT1) and ten with clinical T2 (cT2) colon cancer and without suspicion of metastases were included. Failure occurred in two patients due to incomplete resections. Histopathological examination classified seven patients as having pT1, nine as pT2, six as pT3 adenocarcinomas, and three as non-invasive tumors. In three patients, the surgical strategy was changed intraoperatively to conventional colectomy due to tumor location or size. Median length of stay was 1 day. Seven patients had completion colectomy performed due to histological high-risk factors. None had LNM. Conclusions In selected patients, CELS resection was feasible, and could spare some patients large bowel resection

Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer

The spread of early-stage (T1 and T2) adenocarcinomas to locoregional lymph nodes is a key event in disease progression of colorectal cancer (CRC). The cellular mechanisms behind this event are not completely understood and existing predictive biomarkers are imperfect. Here, we used an end-to-end deep learning algorithm to identify risk factors for lymph node metastasis (LNM) status in digitized histopathology slides of the primary CRC and its surrounding tissue. In two large population-based cohorts, we show that this system can predict the presence of more than one LNM in pT2 CRC patients with an area under the receiver operating curve (AUROC) of 0.733 (0.67-0.758) and patients with any LNM with an AUROC of 0.711 (0.597-0.797). Similarly, in pT1 CRC patients, the presence of more than one LNM or any LNM was predictable with an AUROC of 0.733 (0.644-0.778) and 0.567 (0.542-0.597), respectively. Based on these findings, we used the deep learning system to guide human pathology experts towards highly predictive regions for LNM in the whole slide images. This hybrid human observer and deep learning approach identified inflamed adipose tissue as the highest predictive feature for LNM presence. Our study is a first proof of concept that artificial intelligence (AI) systems may be able to discover potentially new biological mechanisms in cancer progression. Our deep learning algorithm is publicly available and can be used for biomarker discovery in any disease setting. (c) 2021 The Pathological Society of Great Britain and Ireland