Prediction of Ovarian Cancer Histotypes and Surgical Outcome

Official title

Prediction of Ovarian Cancer Histotypes and Surgical Outcome Through Artificial Intelligence

Overview

The standard treatment for advanced ovarian cancer (AOC) is primary cytoreductive surgery (PCS) followed by adjuvant chemotherapy. However, optimal cytoreduction is not always achievable, particularly in cases with high tumor burden or patient frailty. In such scenarios, neoadjuvant chemotherapy (NACT) followed by interval cytoreductive surgery (ICS) represents a valid alternative with comparable oncologic outcomes in selected patients. To optimize surgical strategy, objective tools are needed to identify the best candidates for PCS. Scoring systems such as the Fagotti Score and the Predictive Index Value (PIV) assess tumor resectability, but their accuracy largely depends on surgeon expertise. It has already developed the PREDAtOOR project, a significant advancement in the use of artificial intelligence (AI) for managing AOC. PREDAtOOR has demonstrated high accuracy in both predicting the Fagotti Score and segmenting lesions from diagnostic laparoscopy videos, thus supporting a more objective and reproducible surgical decision-making process. Importantly, therapeutic strategies should also consider tumor biology, as the response to NACT varies across histological and molecular subtypes. Unfortunately, such information is usually derived from histopathological and genomic analyses performed only after the surgical decision. Kurman and Shih proposed a dualistic model of epithelial ovarian tumors, with distinct clinical and molecular features: Type I tumors (low-grade serous, endometrioid, clear cell, mucinous): indolent growth, typically confined to the ovary, with stable genomes. Early-stage cases may be cured surgically. Metastatic Type I tumors tend to be chemoresistant but may respond to targeted therapies. Type II tumors (high-grade serous carcinoma \[HGSC\], carcinosarcomas, undifferentiated carcinomas): aggressive behavior, marked genomic instability, and frequent homologous recombination deficiency (HRD). Although initially sensitive to platinum-based chemotherapy and PARP inhibitors, resistance often emerges. Among these, HGSC is the most frequent and lethal. Yet, even within HGSC, substantial variability in chemotherapy response and clinical outcome is observed. A recent morphologic classification of HGSC stratifies tumors into infiltrative vs. expansive patterns, associated with specific molecular alterations and therapeutic responses. However, these morphological and molecular features are not yet integrated into intraoperative decision-making, highlighting a need for new intraoperative tools to personalize care. In this precision medicine landscape, AI, particularly through machine learning and computer vision, offers powerful solutions. These technologies can process large, heterogeneous datasets and automate intraoperative assessments, enhancing objectivity and diagnostic reproducibility. While AI-based classification of histologic and molecular subtypes from laparoscopy remains largely unexplored, it holds the potential to revolutionize treatment stratification in AOC.

Interventions

• Other Diagnostic Laparoscopy videos
  Diagnostic laparoscopy videos will be collected and stored on internal hard drives. Pseudo-anonymized laparoscopic videos will be annotated by expert clinicians. Artificial intelligence (AI)-based solutions will be developed, trained, and validated.

Primary outcome measures

• Accuracy of Computer Vision Algorithm in Predicting Ovarian Cancer Histotype [Time frame: 36 months]

Secondary outcome measures (4)

• Accuracy of Computer Vision Algorithm in Predicting Morphological Classification [Time frame: 36 months]
• Accuracy of Computer Vision Algorithm in Predicting Molecular and Genetic Tumor Profiles [Time frame: 36 months]
• Accuracy of Computer Vision Algorithm in Predicting Chemosensitivity or Chemoresistance in High-Grade Serous Ovarian Cancer (HGSOC) [Time frame: 36 months]
• Accuracy of Computer Vision Algorithm in Predicting the Feasibility of Achieving Complete Gross Resection (CGR) [Time frame: 36 months]

Eligibility criteria

Inclusion criteria

• Patients over 18 years of age
• Patients fit for upfront cytoreductive surgery.
• Patients undergoing diagnostic laparoscopy as part of the upfront decision-making algorithm.
• Patients with a primary diagnosis of advanced ovarian carcinoma, FIGO stage IIIB - IVB
• Signature of the informed consent / consent for the processing of personal data and associated data for research purposes in patients treated at the Fondazione Policlinico Universitario A. Gemelli IRCCS (form 743 or form pro.1145.001) / substitute declaration for the consent form for deceased patients.

Exclusion criteria

• Lack of information on surgical outcome and clinical-pathological characteristics.
• Ovarian carcinoma patients without evidence of macroscopic peritoneal carcinomatosis (FIGO stage I-IIIA).
• Secondary cytoreductive surgery.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model

Cohort

Study locations

Center list to be confirmed — check the primary protocol.

Identifiers

NCT: NCT07057167 · 7504

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗