CIRDataset: A large-scale Dataset for Clinically-Interpretable lung nodule Radiomics and malignancy prediction

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The CIRDataset provides a large-scale dataset of 956 annotated lung nodules with segmentations and classifications of spiculations and lobulations, which are important radiomic features for assessing malignancy. It aims to address the lack of publicly available datasets capturing these subtle radiological features typically assessed by radiologists but often smoothed over by deep learning segmentation models. The dataset is accompanied by code, models, and a pipeline to enable the development of AI systems for joint nodule segmentation, classification of spiculations/lobulations, and malignancy prediction using an end-to-end deep learning approach.

Health & Medicine

CIRDataset: A large-scale Dataset for Clinically Interpretable lung nodule
Radiomics and malignancy prediction
Wookjin Choi1, Navdeep Dahiya2, Saad Nadeem2
1Department of Radiation Oncology, Thomas Jefferson University, 2Department of Medical Physics, Memorial Sloan Kettering Cancer Center
https://github.com/choilab-jefferson/CIR
wookjin.choi@jefferson.edu
nadeems@mskcc.org
Clinically Interpretable Radiomics
(https://github.com/choilab-jefferson/CIR)
(1) Public AI-ready training/testing data
(2) Documentation & code (PyTorch)
(3) Pretrained models/docker containers
(4) Pipeline – create annotations from scratch
(5) Reproducibility [MICCAI’22]
Motivation
• Spiculations/lobulations,
routinely assessed by
radiologists, are good
predictors of malignancy
• Manual annotation of these
features is a tedious task;
thus, no datasets exist to
probe their importance in
malignancy prediction
• Most DL segmentation
algorithms tend to smooth
these features out (even
though critical for clinical
reporting – LungRADS)
Approach
• Release large-scale dataset,
containing 956 annotations
on segmented lung nodules
(TCIA LIDC and LUNGx)
• End-to-end DL algorithm for
nodule segmentation, spikes’
classification and
malignancy prediction
Results
Multi-class Voxel2Mesh Extension. Mesh encoder-
decoder architecture with extra decoder layers for spikes
classification and malignancy prediction
Nodule (Class0), Spiculation (Class1), Lobulation (Class2)
Spike classification metrics
Malignancy prediction metrics

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CIRDataset: A large-scale Dataset for Clinically-Interpretable lung nodule Radiomics and malignancy prediction

1. CIRDataset: A large-scale Dataset for Clinically Interpretable lung nodule Radiomics and malignancy prediction Wookjin Choi1, Navdeep Dahiya2, Saad Nadeem2 1Department of Radiation Oncology, Thomas Jefferson University, 2Department of Medical Physics, Memorial Sloan Kettering Cancer Center https://github.com/choilab-jefferson/CIR wookjin.choi@jefferson.edu nadeems@mskcc.org Clinically Interpretable Radiomics (https://github.com/choilab-jefferson/CIR) (1) Public AI-ready training/testing data (2) Documentation & code (PyTorch) (3) Pretrained models/docker containers (4) Pipeline – create annotations from scratch (5) Reproducibility [MICCAI’22] Motivation • Spiculations/lobulations, routinely assessed by radiologists, are good predictors of malignancy • Manual annotation of these features is a tedious task; thus, no datasets exist to probe their importance in malignancy prediction • Most DL segmentation algorithms tend to smooth these features out (even though critical for clinical reporting – LungRADS) Approach • Release large-scale dataset, containing 956 annotations on segmented lung nodules (TCIA LIDC and LUNGx) • End-to-end DL algorithm for nodule segmentation, spikes’ classification and malignancy prediction Results Multi-class Voxel2Mesh Extension. Mesh encoder- decoder architecture with extra decoder layers for spikes classification and malignancy prediction Nodule (Class0), Spiculation (Class1), Lobulation (Class2) Spike classification metrics Malignancy prediction metrics

CIRDataset: A large-scale Dataset for Clinically-Interpretable lung nodule Radiomics and malignancy prediction

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