ADDS: A Multimodal AI Platform for Precision Oncology

ADDS (AI-Driven Decision Support) is a multimodal AI platform that integrates CT tumor segmentation, endothelial cell morphology quantification, and clinical decision support, providing an end-to-end solution for precision oncology from image analysis to drug synergy prediction. It integrates multi-dimensional data through a unified framework to assist in tumor diagnosis, research, and treatment decision-making, demonstrating the integrated application value of AI technology in the field of precision oncology.

Precision oncology aims to develop personalized plans based on patients' molecular, pathological, and clinical characteristics, but it faces three major challenges: high heterogeneity of tumor data (requiring specialized handling of different data types), difficulty in integrating scattered data, and the need for complex algorithms to extract clinical insights from multimodal data. The ADDS platform addresses these challenges by integrating image analysis, cell morphology quantification, and drug synergy prediction through a unified framework, providing end-to-end technical support.

ADDS consists of three core modules:

1. CT Tumor Detection Module: Based on Swin-UNETR and nnUNet models, combined with HU threshold (60-120 HU) and morphological filtering for tumor segmentation and detection;
2. Cell Morphometry Module: Uses Cellpose v3 to segment cells and analyze the morphological features (area, perimeter, etc.) of human umbilical vein endothelial cells (HUVEC);
3. Drug Synergy Prediction Module: Integrates TCGA-COAD, DrugComb, and OncoKB data to build machine learning models, calculates synergy indices such as Bliss, Loewe, HSA, and ZIP, and uses 5-fold cross-validation for training.

Validation data for each module:

1. CT Module: Achieved 98.65% slice-level detection accuracy (95% confidence interval [0.949, 1.000]) on 74 CT slices (single patient, arterial phase) from the colorectal cancer cohort at Inha University Hospital;
2. Cell Module: Analyzed 80 bright-field microscopy images, successfully segmented and quantified morphological features of 43,190 cells (including 4 experimental groups such as control group and healthy serum group);
3. Drug Module: Trained on 2285 samples from TCGA, and evaluated the model using 5-fold cross-validation.

The value of ADDS lies in its integration of multimodal data to build a comprehensive tumor profile and assist clinical decision-making. However, it has limitations: the validation data scale of each module is limited (single patient for CT module, in vitro model for cell module, no prospective clinical validation for drug module); the degree of integration between modules needs to be improved; clinical process integration and regulatory compliance need further refinement.

Future directions for ADDS include: expanding the scale of validation datasets, conducting multi-center clinical studies, developing patient-derived organoid validation platforms, optimizing multimodal fusion algorithms, and exploring integration with electronic medical record systems. In terms of clinical translation prospects, the platform architecture is ready for clinical integration and can assist radiology departments (improving tumor detection rates), pathology departments (automated cell analysis), and oncology departments (personalized medication support), but strict validation processes are required.