Prediction of Ovarian Cancer Histotypes and Surgical Outcome
- Conditions
- Ovarian CancerMetastatic Ovarian Carcinoma
- Registration Number
- NCT07057167
- Brief Summary
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.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- NOT_YET_RECRUITING
- Sex
- Female
- Target Recruitment
- 100
Not provided
Not provided
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Accuracy of Computer Vision Algorithm in Predicting Ovarian Cancer Histotype 36 months Proportion (%) of laparoscopic videos in which the computer vision algorithm correctly predicts the histotype of ovarian cancer (Non-Epithelial vs Epithelial, and Epithelial subtypes: Type I vs Type II), using final histopathological diagnosis as the reference standard.
- Secondary Outcome Measures
Name Time Method Accuracy of Computer Vision Algorithm in Predicting Morphological Classification 36 months Proportion (%) of laparoscopic videos of high-grade serous ovarian cancer (HGSOC) in which the computer vision algorithm correctly classifies the tumor into two distinct morphological subtypes (as defined by Handley et al.) during diagnostic laparoscopy, using expert pathological assessment as the reference standard.
Accuracy of Computer Vision Algorithm in Predicting Molecular and Genetic Tumor Profiles 36 months Proportion (%) of laparoscopic videos in which the computer vision algorithm correctly predicts molecular and genetic tumor profiles (homologous recombination deficiency \[HRD\] status, homologous recombination proficiency \[HRP\] status, and BRCA mutation status) using molecular/genetic testing as the reference standard.
Accuracy of Computer Vision Algorithm in Predicting Chemosensitivity or Chemoresistance in High-Grade Serous Ovarian Cancer (HGSOC) 36 months Proportion (%) of laparoscopic videos of high-grade serous ovarian cancer (HGSOC) in which the computer vision algorithm correctly predicts chemosensitivity (platinum-free interval \[PFI\] \> 6 months) or chemoresistance (PFI \< 6 months), using clinical follow-up as the reference standard.
Accuracy of Computer Vision Algorithm in Predicting the Feasibility of Achieving Complete Gross Resection (CGR) 36 months Proportion (%) of laparoscopic videos in which the computer vision algorithm correctly predicts the feasibility of achieving complete gross resection (CGR; defined as no visible residual disease at the end of surgery), compared with the actual surgical outcome documented by surgical reports