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

arXiv: https://arxiv.org/abs/2206.14903 · HTML: https://mlsanity.com/paper/2206.14903/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 29 Jun 2022.

## Builds on (most cited first, 3 of 3)

- [Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data](https://mlsanity.com/paper/1912.03681/) (2019)
- [Highly accurate model for prediction of lung nodule malignancy with CT scans](https://mlsanity.com/paper/1802.01756/) (2018)
- [Characterization of Lung Nodule Malignancy using Hybrid Shape and Appearance Features](https://mlsanity.com/paper/1609.06668/) (2016)

## Built on by (most cited first, 6 of 6)

- [Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets](https://mlsanity.com/paper/2304.09324/) (2023)
- [SAM2 for Image and Video Segmentation: A Comprehensive Survey](https://mlsanity.com/paper/2503.12781/) (2025)
- [U-Netmer: U-Net meets Transformer for medical image segmentation](https://mlsanity.com/paper/2304.01401/) (2023)
- [FunnyNodules: A Customizable Medical Dataset Tailored for Evaluating Explainable AI](https://mlsanity.com/paper/2511.15481/) (2025)
- [Parse and Recall: Towards Accurate Lung Nodule Malignancy Prediction like Radiologists](https://mlsanity.com/paper/2307.10824/) (2023)
- [Lung-CADex: Fully automatic Zero-Shot Detection and Classification of Lung Nodules in Thoracic CT Images](https://mlsanity.com/paper/2407.02625/) (2024)

## Abstract (arXiv)

Spiculations/lobulations, sharp/curved spikes on the surface of lung nodules, are good predictors of lung cancer malignancy and hence, are routinely assessed and reported by radiologists as part of the standardized Lung-RADS clinical scoring criteria. Given the 3D geometry of the nodule and 2D slice-by-slice assessment by radiologists, manual spiculation/lobulation annotation is a tedious task and thus no public datasets exist to date for probing the importance of these clinically-reported features in the SOTA malignancy prediction algorithms. As part of this paper, we release a large-scale Clinically-Interpretable Radiomics Dataset, CIRDataset, containing 956 radiologist QA/QC'ed spiculation/lobulation annotations on segmented lung nodules from two public datasets, LIDC-IDRI (N=883) and LUNGx (N=73). We also present an end-to-end deep learning model based on multi-class Voxel2Mesh extension to segment nodules (while preserving spikes), classify spikes (sharp/spiculation and curved/lobulation), and perform malignancy prediction. Previous methods have performed malignancy prediction for LIDC and LUNGx datasets but without robust attribution to any clinically reported/actionable features (due to known hyperparameter sensitivity issues with general attribution schemes). With the release of this comprehensively-annotated CIRDataset and end-to-end deep learning baseline, we hope that malignancy prediction methods can validate their explanations, benchmark against our baseline, and provide clinically-actionable insights. Dataset, code, pretrained models, and docker containers are available at https://github.com/nadeemlab/CIR.