Assessment of intraoperative scoring systems for predicting cytoreduction outcome in peritoneal metastatic disease : a systematic review and meta-analysis

BackgroundCytoreductive surgery (CRS) is a widely acknowledged treatment approach for peritoneal metastasis, showing favorable prognosis and long-term survival. Intraoperative scoring systems quantify tumoral burden before CRS and may predict complete cytoreduction (CC). This study reviews the intraoperative scoring systems for predicting CC and optimal cytoreduction (OC) and evaluates the predictive performance of the Peritoneal Cancer Index (PCI) and Predictive Index Value (PIV).MethodsSystematic searches were conducted in Embase, MEDLINE, and Web of Science. Meta-analyses of extracted data were performed to compare the absolute predictive performances of PCI and PIV.ResultsThirty-eight studies (5834 patients) focusing on gynecological (n = 34; 89.5%), gastrointestinal (n = 2; 5.3%) malignancies, and on tumors of various origins (n = 2; 5.3%) were identified. Seventy-seven models assessing the predictive performance of scoring systems (54 for CC and 23 for OC) were identified with PCI (n = 39/77) and PIV (n = 16/77) being the most common. Twenty models (26.0%) reinterpreted previous scoring systems of which ten (13%) used a modified version of PIV (reclassification). Meta-analyses of models predicting CC based on PCI (n = 21) and PIV (n = 8) provided an AUC estimate of 0.83 (95% confidence interval [CI] 0.79-0.86; Q = 119.6, p = 0.0001; I2 = 74.1%) and 0.74 (95% CI 0.68-0.81; Q = 7.2, p = 0.41; I2 = 11.0%), respectively.ConclusionsPeritoneal Cancer Index models demonstrate an excellent estimate of CC, while PIV shows an acceptable performance. There is a need for high-quality studies to address management differences, establish standardized cutoff values, and focus on non-gynecological malignancies

Towards abdominal 3-D scene rendering from laparoscopy surgical videos using NeRFs

Given that a conventional laparoscope only provides a two-dimensional (2-D) view, the detection and diagnosis of medical ailments can be challenging. To overcome the visual constraints associated with laparoscopy, the use of laparoscopic images and videos to reconstruct the three-dimensional (3-D) anatomical structure of the abdomen has proven to be a promising approach. Neural Radiance Fields (NeRFs) have recently gained attention thanks to their ability to generate photo-realistic images from a 3-D static scene, thus facilitating a more comprehensive exploration of the abdomen through the synthesis of new views. This distinguishes NeRFs from alternative methods such as Simultaneous Localization and Mapping (SLAM) and depth estimation. In this paper, we present a comprehensive examination of NeRFs in the context of laparoscopy surgical videos, with the goal of rendering abdominal scenes in 3-D. Although our experimental results are promising, the proposed approach encounters substantial challenges, which require further exploration in future research

Standardizing eligibility and patient selection for Pressurized Intraperitoneal Aerosol Chemotherapy: A Delphi consensus statement.

Pressurized Intraperitoneal Aerosol Chemotherapy (PIPAC) is a procedure for minimally invasive drug administration in patients with peritoneal metastasis. Previous studies have emphasized the importance of uniformity in treatment protocols and standardization of this practice. This study aimed to reach a consensus on eligibility, patient selection, and choice of chemotherapy for PIPAC. A three-round modified Delphi study was conducted. A steering group formulated a list of baseline statements, addressing the objectives. The steering group consisted of seven expert surgical and medical oncologists. Available evidence and published key opinions were critically reviewed. An international expert panel scored those statements on a 4-point Likert scale. The statements were submitted electronically and anonymously. Consensus was reached if the agreement rate was ≥75%. A minimum Cronbach's alpha of >0.8 was set. Forty-five (45/58; 77.6%) experts participated and completed all rounds. Experts were digestive surgeons (n = 28), surgical oncologists (n = 7), gynecologists (n = 5), medical oncologists (n = 4), and one clinical researcher. Their assessment of 81 preliminary statements in the first round resulted in 41 consolidated statements. In round two, consensus was reached on 40 statements (40/41; 97.6%) with a consensus of ≥80% for each individual statement. In the third round, 40 statements were unanimously approved as definitive. The choice of first- and second-line chemotherapy remained controversial and could not reach consensus. This International Delphi study provides practical guidance on eligibility and patient selection for PIPAC. Ongoing trial data and long-term results that could contribute to the further standardization of PIPAC are eagerly awaited

Predictive factors for survival in borderline resectable and locally advanced pancreatic cancer: are these really two different entities?

Abstract Background The treatment of borderline resectable (BR) and locally advanced (LA) pancreatic ductal adenocarcinoma (PDAC) has evolved with a wider application of neoadjuvant chemotherapy (NACHT). The aim of this study was to identify predictive factors for survival in BR and LA PDAC. Methods Clinicopathologic data of patients with BR and LA PDAC who underwent surgical exploration between January 2011 and June 2021 were retrospectively collected. Survival from the date of surgery was estimated using the Kaplan-Meier method. Simple and multiple Cox proportional hazards models were fitted to identify factors associated with survival. Surgical resection was analyzed in combination with the involvement of lymph nodes as this last was only known after a formal resection. Results Ninety patients were surgically explored (BR: 45, LA: 45), of which 51 (57%) were resected (BR: 31, LA: 20). NACHT was administered to 43 patients with FOLFIRINOX being the most frequent regimen applied (33/43, 77%). Major complications (Clavien-Dindo grade III and IV) occurred in 7.8% of patients and 90-day mortality rate was 3.3%. The median overall survival since surgery was 16 months (95% CI 12-20) in the group which underwent surgical resection and 10 months (95% CI 7-13) in the group with an unresectable tumor (p=0.001). Cox proportional hazards models showed significantly lower mortality hazard for surgical resection compared to no surgical resection, even after adjusting for National Comprehensive Cancer Network  (NCCN) classification and administration of NACHT [surgical resection with involved lymph nodes vs no surgical resection (cHR 0.49; 95% CI 0.29-0.82; p=0.007)]. There was no significant difference in survival between patients with BR and LA disease (cHR= 1.01; 95% CI 0.63-1.62; p=0.98). Conclusions Surgical resection is the only predictor of survival in patients with BR and LA PDAC, regardless of their initial classification as BR or LA. Our results suggest that surgery should not be denied to patients with LA PDAC a priori. Prospective studies including patients from the moment of diagnosis are required to identify biologic and molecular markers which may allow a better selection of patients who will benefit from surgery

A reference-based approach for tumor size estimation in monocular laparoscopic videos

Laparoscopic exploration of the abdominal cavity is rou tinely performed for the diagnosis, assessment, and staging of peritoneal metastasis (PM). Accurately measuring tumor size during this procedure is crucial for prognosis and treatment planning. As conventional approaches for tumor size measurement rely on subjective manual assessments during or after surgery, they stand to benefit from computer assistance. This study proposes a new method for measuring tumor size in laparoscopic monocular videos. Specifically, we introduce a novel mathematical equation that connects the intrinsic parameters of a monocular camera, the surface area of target and reference objects, and their distances to the camera. Furthermore, we combine this equation with an object segmentation model (Mask2Former) and a depth estimation model (MiDaS), creating an end-to-end framework that automates tumor size measurement in monocular laparoscopic videos. We evaluate the proposed method using a laparoscopy dataset comprising 18 videos depicting 76 tumor biopsies, with tumor size measured by surgeons who are experts in laparoscopic surgery. When estimating the size of the various tumors in this dataset, we obtain a Mean Absolute Error (MAE) of 2.44 mm ± 0.23 mm, demonstrating that the newly proposed method accurately predicts intraoperative tumor size. Our code and the evaluation dataset are publicly available on https://github.com/amiiiirrrr/TSEML

Postgraduate surgical education in East, Central, and Southern Africa : a needs assessment survey

BACKGROUND:The Lancet Commission on Global Surgery has identified workforce development as an important component of National Surgical Plans to advance the treatment of surgical disease in low- and middle-income countries. The goal of our study is to identify priorities of surgeon educators in the region so that collaboration and intervention may be appropriately targeted. STUDY DESIGN:The American College of Surgeons Operation Giving Back, in collaboration with leaders of the College of Surgeons of Eastern, Central and Southern Africa (COSECSA), developed a survey to assess the needs and limitations of surgical educators working under their organizational purview. COSECSA members were invited to complete an online survey to identify and prioritize factors within 5 domains: (1) Curriculum Development, (2) Faculty Development, (3) Structured Educational Content, (4) Skills and Simulation Training, and (5) Trainee Assessment and Feedback. RESULTS:One-hundred sixty-six responses were received after 3 calls for participation, representing all countries in which COSECSA operates. The majority of respondents (78%) work in tertiary referral centers. Areas of greatest perceived need were identified in the Faculty Development and Skills and Simulation domains. Although responses differed between domains, clinical responsibilities, cost, and technical support were commonly cited as barriers to development. CONCLUSIONS:This needs assessment identified educational needs and priorities of COSECSA surgeons. Our study will serve as a foundation for interventions aimed at further improving graduate surgical education and ultimately patient care in the region

Development of a video-based deep learning model for differentiation of malignant and benign lesions during staging laparoscopy : is the machine better than the expert?

Background: Laparoscopic staging of abdominal cancer is routinely performed to assess the presence of peritoneal metastasis (PM). One major challenge of laparoscopic staging is the ability to distinguish between a malignant and benign peritoneal lesion, particularly scar tissue. Lesions' recognition is subjective to intra-case variability, surgeons' experience, type of primary tumor and response to systemic chemotherapy. The Peritoneal Regression Grading Score (PRGS) offers an objective histologic evaluation of the biopsied lesion by assessing the complete or partial presence of malignant cells (PRGS 2-3-4) and fibrous or benign lesion (PRGS 1). Machine learning-based (ML-based) computer vision has various applications in the medical field and can be useful in deducing clinical information through surgical video analysis. The aim of this study was to develop a ML model that can aid the surgeon in the intra-operative assessment of PM. Methods: Retrospectively collected videos of laparoscopies for the staging of PM before cytoreductive surgery or PIPAC were screened from the institutional database for the presence of recorded biopsies. PRGS from the pathology report of these biopsies was retrieved and reviewed by a pathologist. The surgical phase of the biopsy was annotated by systematic selection of 200 frames before the closure of the biopsy instrument on the tissue. One single frame was selected from each timeframe based on its representativeness. Two trained annotators performed the selection, one surgical expert reviewed each annotation. Discrepancies were solved by consensus. ML models based on multiple (ResEfficientnetV2_L) and single frames (Resnet50) per biopsy were trained on the PRGS using a one-versus-all binary classification. Five-fold cross-validation was applied. Two oncologic surgeons, experts in treating and assessing PM, blindly and independently scored the biopsies with PRGS. Results: A total of 127 videos from 67 patients were identified, showcasing PM of gastric, colorectal, appendix, hepatic, gallbladder, breast carcinomas, primary peritoneal tumors and benign lesions. Annotation was performed on 463 biopsies: PRGS 1: 204, PRGS 2: 164, PRGS 3: 70, PRGS 4: 25. Based on timeframe annotation 5214 (PRGS 1: 2044, PRGS 2: 1814, PRGS 3: 929, PRGS 4: 427) images were identified. The model trained on one-versus-all classification for single and multiple images showed an accuracy of 52.4% (Precision 56.2, Recall 48.2) and 45.7% (Precision 76.8, Recall 36.0) respectively in the validation set. Accuracy of each expert was 36.0% and 30.5%. Conclusions: While the accuracy of computer vision models may not be clinically acceptable as a substitute for biopsies, they outperform surgeons in differentiating PM from benign lesions. Computer vision models might be of aid for the surgeon in the recognition of these lesions

National practice patterns in the use of endoscopic ultrasound biopsy for resectable Pancreatic Neuroendocrine Tumors : insights into the role of DOTATATE PET/CT in diagnosis

Introduction Pancreatic neuroendocrine tumors (PNETs) are typically diagnosed using endoscopic ultrasound-guided (EUS) biopsy, which can be associated with complications. Since 2016, DOTATATE PET/CT has emerged as an effective tool to localize and stage PNETs. Methods Patients with PNETs who underwent R0 resections were identified from the 2004–2019 National Cancer Database PUF. Joinpoint regression and multivariable logistic regression were used to analyze trends in the use of biopsy. Results Of 16,746 R0 resected PNET patients, 44 ​% underwent diagnostic biopsy. Joinpoint regression showed a significant increase in the use of biopsy from 2004 to 2019 (APC 1.80, p ​< ​0.001). A higher percentage of patients diagnosed after DOTATATE approval underwent biopsy compared to those diagnosed before (48 ​% vs. 42 ​%, p ​< ​0.001). Adjusted analysis showed diagnosis after 2016 was associated with increased odds of biopsy (OR ​= ​1.67, p ​< ​0.001). Conclusions Despite technologic advancement with DOTATATE PET/CT, there has been a significant increase in the proportion of resectable PNETs undergoing preoperative biopsy