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Updated: Mar 4, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Automated Radiomics Model for Preoperative Pancreatic Neuroendocrine Tumor Grade Prediction.

Pratik Chandra¹, Hadi Ghahremannezhad¹, Naaz Nasar¹

¹Department of Surgery, Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Annals of Surgical Oncology

|March 2, 2026

Summary

This summary is machine-generated.

An automated radiomics pipeline accurately predicts pancreatic neuroendocrine tumor (PNET) grade noninvasively. This approach aids preoperative decision-making by identifying tumor characteristics from CT scans.

Keywords:

Grade prediction Lesions < 2 cm Pancreatic neuroendocrine tumors Radiomics Resection

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Last Updated: Mar 4, 2026

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Multianimal Magnetic Resonance Imaging for Tumor Measurements in Pancreatic Cancer Mouse Models

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Published on: February 3, 2026

Area of Science:

Radiology
Oncology
Medical Imaging

Background:

Pancreatic neuroendocrine tumor (PNET) behavior is influenced by grade and genomics.
Radiomics offers a noninvasive method to assess these tumor characteristics.
Predicting PNET grade preoperatively is crucial for treatment planning.

Purpose of the Study:

To develop an automated pipeline for radiomics analysis.
To preoperatively predict pancreatic neuroendocrine tumor (PNET) grade using radiomics.
To explore associations between radiomic and genomic features.

Main Methods:

An automated segmentation model was trained using manually segmented CT scans.
A radiomics model was developed to predict PNET grade (I vs. II/III).
The model's performance was evaluated on an independent test cohort using automatically segmented scans.

Main Results:

The automated segmentation achieved 90% success in the test cohort.
The radiomics model demonstrated an AUC of 0.85 for grade prediction.
Significant associations were found between specific genomic alterations (DAXX, ATRX loss) and radiomic features.

Conclusions:

The automated pipeline successfully segmented PNETs and accurately predicted tumor grade.
Radiomics, integrated into an automated workflow, can enhance preoperative PNET assessment.
This technology holds potential for improving clinical decision-making in PNET management.