The Learning Curve for Robotic Lateral Pelvic Lymph Node Dissection for Rectal Cancer: A View from the West.

BACKGROUND: Lateral pelvic lymph-node dissection is performed for selected patients with rectal cancer with persistent lateral nodal disease after neoadjuvant therapy. This technique has been slow to be adopted in the West due to concerns regarding technical difficulty. This is the first report on the learning curve for lateral pelvic lymph node dissection in the US or Europe. OBJECTIVE: The aim of this study was to analyze the learning curve associated with robotic lateral pelvic lymph node dissection. DESIGN: Retrospective observational cohort. SETTING: Tertiary academic cancer center. PATIENTS: Consecutive patients from 2012 to 2021. INTERVENTION: All patients underwent robotic lateral pelvic lymph node dissection. MAIN OUTCOME MEASURES: The primary endpoints were the learning curves for maximum number of nodes retrieved and urinary retention which was evaluated with simple cumulative-sum and two-sided Bernoulli cumulative-sum charts. RESULTS: Fifty-four procedures were included. A single-surgeon (n = 35) and an institutional learning curve are presented in the analysis. In the single-surgeon learning curve, a turning point marking the end of a learning phase was detected at the 12th procedure for the number of retrieved nodes and at the 20th for urinary retention. In the institutional learning curve analysis, two turning points were identified at the 13th and 26th procedures indicating progressive improvements for the number of retrieved nodes and at the 27th for urinary retention. No sustained alarm signals were detected at any time point. LIMITATIONS: The retrospective nature, small sample size and the referral center nature of the reporting institution that may limit generalizability. CONCLUSIONS: In a setting of institutional experience with robotic colorectal surgery including beyond TME resections, the learning curve for robotic lateral pelvic lymph node dissection is acceptably short. Our results demonstrate feasibility of acquisition of this technique in a controlled setting, with sufficient case volume and proctoring can optimize the learning curve. See Video Abstract.

Early Postoperative Prediction of Complications and Readmission After Colorectal Cancer Surgery Using an Artificial Neural Network.

BACKGROUND: Early predictors of postoperative complications can risk-stratify patients undergoing colorectal cancer surgery. However, conventional regression models have limited power to identify complex nonlinear relationships among a large set of variables. We developed artificial neural network models to optimize the prediction of major postoperative complications and risk of readmission in patients undergoing colorectal cancer surgery. OBJECTIVE: The aim of this study was to develop an artificial neural network model to predict postoperative complications using postoperative laboratory values, and compare these models' accuracy to standard regression methods. DESIGN: This retrospective study included patients who underwent elective colorectal cancer resection between January 1, 2016, and July 31, 2021. Clinical data, cancer stage, and laboratory data from postoperative day 1 to 3 were collected. Models of complications and readmission risk were created using multivariable logistic regression and single-layer neural networks. SETTING: National Cancer Institute-Designated Comprehensive Cancer Center. PATIENTS: Adult colorectal cancer patients. MAIN OUTCOME MEASURES: Accuracy of predicting postoperative major complication, readmission and anastomotic leak using the area under the receiver-operating characteristic curve. RESULTS: Neural networks had larger areas under the curve for predicting major complications compared to regression models (neural network 0.811; regression model 0.724, p < 0.001). Neural networks also showed an advantage in predicting anastomotic leak (p = 0.036) and readmission using postoperative day 1-2 values (p = 0.014). LIMITATIONS: Single-center, retrospective design limited to cancer operations. CONCLUSIONS: In this study, we generated a set of models for early prediction of complications after colorectal surgery. The neural network models provided greater discrimination than the models based on traditional logistic regression. These models may allow for early detection of postoperative complications as soon as postoperative day 2. See Video Abstract.

Testing Machine Learning Models to Predict Postoperative Ileus after Colorectal Surgery.

Background: Postoperative ileus (POI) is a common complication after colorectal surgery, leading to increased hospital stay and costs. This study aimed to explore patient comorbidities that contribute to the development of POI in the colorectal surgical population and compare machine learning (ML) model accuracy to existing risk instruments. Study Design: In a retrospective study, data were collected on 316 adult patients who underwent colorectal surgery from January 2020 to December 2021. The study excluded patients undergoing multi-visceral resections, re-operations, or combined primary and metastatic resections. Patients lacking follow-up within 90 days after surgery were also excluded. Eight different ML models were trained and cross-validated using 29 patient comorbidities and four comorbidity risk indices (ASA Status, NSQIP, CCI, and ECI). Results: The study found that 6.33% of patients experienced POI. Age, BMI, gender, kidney disease, anemia, arrhythmia, rheumatoid arthritis, and NSQIP score were identified as significant predictors of POI. The ML models with the greatest accuracy were AdaBoost tuned with grid search (94.2%) and XG Boost tuned with grid search (85.2%). Conclusions: This study suggests that ML models can predict the risk of POI with high accuracy and may offer a new frontier in early detection and intervention for postoperative outcome optimization. ML models can greatly improve the prediction and prevention of POI in colorectal surgery patients, which can lead to improved patient outcomes and reduced healthcare costs. Further research is required to validate and assess the replicability of these results.

Total Neoadjuvant Therapy for Rectal Cancer: Which Regimens to Use?

Total neoadjuvant therapy (TNT) is a novel strategy for rectal cancer that administers both (chemo)radiotherapy and systemic chemotherapy before surgery. TNT is expected to improve treatment compliance, tumor regression, organ preservation, and oncologic outcomes. Multiple TNT regimens are currently available with various combinations of the treatments including induction or consolidation chemotherapy, triplet or doublet chemotherapy, and long-course chemoradiotherapy or short-course radiotherapy. Evidence on TNT is rapidly evolving with new data on clinical trials, and no definitive consensus has been established on which regimens to use for improving outcomes. Clinicians need to understand the advantages and limitations of the available regimens for multidisciplinary decision making. This article reviews currently available evidence on TNT for rectal cancer. A decision making flow chart is provided for tailor-made use of TNT regimens based on tumor location and local and systemic risk.

Para-aortic Lymph Node Dissection for Colorectal Cancer: Predicting Pathologic Lymph Node Positivity and Optimizing Outcomes.

INTRODUCTION: In colorectal cancer, the presence of para-aortic lymph nodes (PALN) indicates extraregional disease. Appropriately selecting patients for whom PALN dissection will provide oncologic benefit remains challenging. This study identified factors to predict survival among patients undergoing PALN dissection for colorectal cancer. METHODS: An institutional database was queried for patients who underwent curative-intent resection of clinically positive PALN for colorectal cancer between 2007 and 2020. Preoperative radiologic images were reviewed, and patients who did and did not have positive PALN on final pathology were compared. Survival analysis was performed to evaluate the impact of pathologically positive PALN on recurrence-free (RFS) and overall survival (OS). RESULTS: Of 74 patients who underwent PALN dissection, 51 had PALN metastasis at the time of primary tumor diagnosis, whereas 23 had metachronous PALN disease. Preoperative chemotherapy ± radiotherapy was given in 60 cases (81.1%), and 28 (37.8%) had pathologically positive PALN. Independent factors associated with positive PALN pathology included metachronous PALN disease and pretreatment and posttreatment radiographically abnormal PALN. On multivariable analysis, pathologically positive PALN was significantly associated with decreased RFS (hazard ratio 3.90) and OS (HR 4.49). Among patients with pathologically positive PALN, well/moderately differentiated histology was associated with better OS, and metachronous disease trended toward an association with better OS. CONCLUSIONS: Pathologically positive PALN are associated with poorer RFS and OS after PALN dissection for colorectal cancer. Clinicopathologic factors may predict pathologic PALN positivity. Curative-intent surgery may provide benefit, especially in patients with well-to-moderately differentiated primary tumors and possibly metachronous PALN disease.

Anatomic Basis of Rectal Cancer Staging: Clarifying Controversies and Misconceptions.

Rectal MRI provides a detailed depiction of pelvic anatomy; specifically, the relationship of the tumor to key anatomic structures, including the mesorectal fascia, anterior peritoneal reflection, and sphincter complex. However, anatomic inconsistencies, pitfalls, and confusion exist, which can have a strong impact on interpretation and treatment. These areas of confusion include the definition of the rectum itself, specifically differentiation of the rectum from the anal canal and the sigmoid colon, and delineation of the high versus low rectum. Other areas of confusion include the relative locations of the mesorectal fascia and peritoneum and their significance in staging and treatment, the difference between the mesorectal fascia and circumferential resection margin, involvement of the sphincter complex, and evaluation of lateral pelvic lymph nodes. The impact of these anatomic inconsistencies and sources of confusion is significant, given the importance of MRI in depicting the anatomic relationship of the tumor to critical pelvic structures, to triage surgical resection and neoadjuvant chemoradiotherapy with the goal of minimizing local recurrence. Evolving treatment paradigms also place MRI central in management of rectal cancer. ©RSNA, 2024.

The current multidisciplinary management of rectal cancer.

Multidisciplinary management of rectal cancer has rapidly evolved over the last several years. This review describes recent data surrounding total neoadjuvant therapy, organ preservation, and management of lateral pelvic lymph nodes. It then presents our treatment algorithm for management of rectal cancer at The University of Texas MD Anderson Cancer Center in the context of this and other existing literature. As part of this discussion, the review describes how we tailor management based upon both patient and tumor-related factors in an effort to optimize patient outcomes.

Perioperative and oncological outcomes following robotic en bloc multivisceral resection for colorectal cancer.

AIM: As multidisciplinary treatment strategies for colorectal cancer have improved, aggressive surgical resection has become commonplace. Multivisceral and extended resections offer curative-intent resection with significant survival benefit. However, limited data exist regarding the feasibility and oncological efficacy of performing extended resection via a minimally invasive approach. The aim of this study was to determine the perioperative and long-term outcomes following robotic extended resection for colorectal cancer. METHOD: We describe the population of patients undergoing robotic multivisceral resection for colorectal cancer at our single institution. We evaluated perioperative details and investigated short- and long-term outcomes, using the Kaplan-Meier method to analyse overall and recurrence-free survival. RESULTS: Among the 86 patients most tumours were T3 (47%) or T4 (47%) lesions in the rectum (78%). Most resections involved the anterior compartment (72%): bladder (n = 13), seminal vesicle/vas deferens (n = 27), ureter (n = 6), prostate (n = 15) and uterus/vagina/adnexa (n = 27). Three cases required conversion to open surgery; 10 patients had grade 3 complications. The median hospital stay was 4 days. Resections were R0 (>1 mm) in 78 and R1 (0 to ≤1 mm) in 8, with none being R2. The average nodal yield was 26 and 48 (55.8%) were pN0. Three-year overall survival was 88% and median progression-free survival was 19.4 months. Local recurrence was 6.1% and distant recurrence was 26.1% at 3 years. CONCLUSION: Performance of multivisceral and extended resection on the robotic platform allows patients the benefit of minimally invasive surgery while achieving oncologically sound resection of colorectal cancer.

Evolving survival gains in patients with young-onset colorectal cancer and synchronous resectable liver metastases.

We aimed to evaluate the practice and the associated outcomes of surgical treatment for young-onset colorectal cancer (YOCRC) patients presenting with synchronous liver metastases. The study cohort was divided into two groups according to surgery date: 131 patients in the early era (EE, 1998-2011) and 179 in the contemporary era (CE, 2012-2020). The CE had a higher rate of node-positive primary tumors, higher carcinoembryonic antigen level, and lower rate of RAS/BRAF mutations. The CE had higher rates of reverse or combined resection, multi-drug prehepatectomy chemotherapy, and two-stage hepatectomy. The median survival was 8.4 years in the CE and 4.3 years in the EE (p = 0.011). On multivariate analysis, hepatectomy in the CE was independently associated with improved overall survival (HR 0.48, p = 0.001). With a combination of perioperative systemic therapy, careful selection of treatment approach, and coordinated resections, durable cure can be achieved in YOCRC patients.