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.

翻译：鉴于结核和2D切片分切评分的3D几何学由放射学家对结核和2D切片分切评分进行了标准化的肺-RADS临床评分标准,因此放射学家定期评估和报告肺癌恶性恶性肿瘤预测值的良好预测。 鉴于结核和2D切片分切评分3D几何学由放射学家对结核和2D切片分切切切切片切片分评分进行3D几何,手动的透视/悬浮注解是一项乏味的任务,因此迄今没有公开的数据集来证明SOTA恶性肿瘤预测算法中临床上报告的这些精度特征的重要性。 作为本文的一部分,我们发布了大规模临床解释性透析性放射性数据数据集,在多级Voxel2Mesh分解法下,我们通过直径直径直径直径直径直径直径直的临床/直径直径直径直径直径直径直径直径直径直径直的直径直径直径直径直径直径直径直径直径直径直径直径直径直径直径直径直径直径直至直至直直至断直直直直直直的结核,同时,而且直直直直直直直直直直至直直直直直直直直直直直直直直直直至直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直地、直至直直直地、直直直直直直直直直直地、直直直至直直直至直直直至直直直直直直直直直直直至直至直至直直直直直直直直至直直直直直直直直至直至直至直至直至直直直直至直直直直直直直直直直直直直直直直直直直直直直至直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直直至直至直直直直直直直直直直直至直至直直直直