An Automated Radiomics Framework for Postoperative Survival Prediction in Colorectal Liver Metastases using Preoperative MRI

TL;DR

An automated AI-based framework for postoperative CRLM survival prediction using pre- and post-contrast MRI and SAMONAI, a prompt propagation algorithm that extends Segment Anything Model to 3D point-based segmentation, are presented.

Abstract

While colorectal liver metastasis (CRLM) is potentially curable via hepatectomy, patient outcomes remain highly heterogeneous. Postoperative survival prediction is necessary to avoid non-beneficial surgeries and guide personalized therapy. In this study, we present an automated AI-based framework for postoperative CRLM survival prediction using pre- and post-contrast MRI. We performed a retrospective study of 227 CRLM patients who had gadoxetate-enhanced MRI prior to curative-intent hepatectomy between 2013 and 2020. We developed a survival prediction framework comprising an anatomy-aware segmentation pipeline followed by a radiomics pipeline. The segmentation pipeline learns liver, CRLMs, and spleen segmentation from partially-annotated data, leveraging promptable foundation models to generate pseudo-labels. To support this pipeline, we propose SAMONAI, a prompt propagation algorithm that extends Segment Anything Model to 3D point-based segmentation. Predicted pre- and post-contrast segmentations are then fed into our radiomics pipeline, which extracts per-tumor features and predicts survival using SurvAMINN, an autoencoder-based multiple instance neural network for time-to-event survival prediction. SurvAMINN jointly learns dimensionality reduction and survival prediction from right-censored data, emphasizing high-risk metastases. We compared our framework against established methods and biomarkers using univariate and multivariate Cox regression. Our segmentation pipeline achieves median Dice scores of 0.96 (liver) and 0.93 (spleen), driving a CRLM segmentation Dice score of 0.78 and a detection F1-score of 0.79. Accurate segmentation enables our radiomics pipeline to achieve a survival prediction C-index of 0.69. Our results show the potential of integrating segmentation algorithms with radiomics-based survival analysis to deliver accurate and automated CRLM outcome prediction.

Figures (11)

• Figure 1: Dataset processing: (a) cohort definitions and exclusion criteria, (b) patient-level data splitting for training, validation, and testing. Patients with manual liver and spleen annotations were held out from segmentation model development, while patients with available genomics data were held out from both segmentation and survival prediction models development to be exclusively used for testing.
• Figure 2: Segmentation pipeline: a promptable foundation model is used to complete partially-annotated masks for a small subset. A few-shot UNETR model is trained on the subset then used to complete the annotations for all cases selected for segmentation model development. Finally, A supervised UNETR model is retrained on the entire training dataset with completed labels.
• Figure 3: Overview of SAMONAI applied to the liver: 3D objects are segmented from single points by propagating prompts from one view to another. Positive and negative points are displayed in green and red, respectively.
• Figure 4: Radiomics pipeline: per-tumor radiomics are extracted. An autoencoder compresses features and a multiple instance regressor connected to the autoencoder's bottleneck predicts per-tumor risk scores and pools them into patient hazards. The autoencoder and regressor are jointly trained using a weighted sum of mean squared error $\mathcal{L}_{\text{\tiny MSE}}$ and cox proportional hazard loss $\mathcal{L}_{\text{\tiny CoxPH}}$.
• Figure 5: Segmentation box plots. MedSAM, in blue, and SAMONAI, in orange, denote prompt-based segmentation performance on missing labels. Pipeline (MedSAM), in green, and Pipeline (SAMONAI), in red, denote final automated segmentation performance after integrating each promptable model into our segmentation pipeline. Black dots inside boxes represent mean Dice scores over patients, and gray dots outside boxes represent individual patients beyond the interquartile range.