DOI: 10.5281/zenodo.7539035
title: AI-derived annotations for the NLST and NSCLC-Radiomics computed tomography imaging collections

description: Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many of the available datasets do not provide annotations of tumors or organs-at-risk, crucial for the assessment of these tools. This is due to the fact that annotation of medical images is time consuming and requires domain expertise. It has been demonstrated that artificial intelligence (AI) based annotation tools can achieve acceptable performance and thus can be used to automate the annotation of large datasets. As part of the effort to enrich the public data available within NCI Imaging Data Commons (IDC) (https://imaging.datacommons.cancer.gov/) [1], we introduce this dataset that consists of such AI-generated annotations for two publicly available medical imaging collections of Computed Tomography (CT) images of the chest. We use publicly available pre-trained AI tools to enhance CT lung cancer collections that are unlabeled or partially labeled. The first tool is the nnU-Net deep learning framework [2] for volumetric segmentation of organs, where we use a pretrained model (Task D18 using the SegTHOR dataset) for labeling volumetric regions in the image corresponding to the heart, trachea, aorta and esophagus. These are the major organs-at-risk for radiation therapy for lung cancer. We further enhance these annotations by computing 3D shape radiomics features using the pyradiomics package [3]. The second tool is a pretrained model for per-slice automatic labeling of anatomic landmarks and imaged body part regions in axial CT volumes [4]. We focus on enhancing two publicly available collections, the Non-small Cell Lung Cancer Radiomics (NSCLC-Radiomics collection) [5,6], and the National Lung Screening Trial (NLST collection) [7,8]. The CT data for these collections are available both in The Cancer Imaging Archive (TCIA) [9] and in NCI Imaging Data Commons (IDC). Further, the NSLSC-Radiomics collection includes expert-generated manual annotations of several chest organs, allowing us to quantify performance of the AI tools in that subset of data. IDC is relying on the DICOM standard to achieve FAIR [10] sharing of data and interoperability. Generated annotations are saved as DICOM Segmentation objects (volumetric segmentations of regions of interest) created using the <em>dcmqi</em> [11], and DICOM Structured Report (SR) objects (per-slice annotations of the body part imaged, anatomical landmarks and radiomics features) created using <em>dcmqi </em>and <em>highdicom</em> [12]. 3D shape radiomics features and corresponding DICOM SR objects are also provided for the manual segmentations available in the NSCLC-Radiomics collection. The dataset shared will be available in IDC, and will be accompanied by a manuscript describing the details of how it was generated, and how the resulting DICOM objects can be interpreted and used in tools. Description of this dataset will be updated accordingly in the future. The annotations are organized as follows. For NSCLC-Radiomics, three nnU-Net models were evaluated ('2d-tta', '3d_lowres-tta' and '3d_fullres-tta'). Within each folder, the PatientID and the StudyInstanceUID are subdirectories, and within this the DICOM Segmentation object and the DICOM SR for the 3D shape features are stored. A separate directory for the DICOM SR body part regression regions ('sr_regions') and landmarks ('sr_landmarks') are also provided with the same folder structure as above. Lastly, the DICOM SR for the existing manual annotations are provided in the 'sr_gt' directory. For NSCLC-Radiomics, each patient has a single StudyInstanceUID. The DICOM Segmentation and SR objects are named according to the SeriesInstanceUID of the original CT files. nsclc 2d-tta PatientID StudyInstanceUID ReferencedSeriesInstanceUID_SEG.dcm ReferencedSeriesInstanceUID_features_SR.dcm 3d_lowres-tta PatientID StudyInstanceUID ReferencedSeriesInstanceUID_SEG.dcm ReferencedSeriesInstanceUID_features_SR.dcm 3d_fullres-tta PatientID StudyInstanceUID ReferencedSeriesInstanceUID_SEG.dcm ReferencedSeriesInstanceUID_features_SR.dcm sr_regions PatientID StudyInstanceUID ReferencedSeriesInstanceUID_regions_SR.dcm sr_landmarks PatientID StudyInstanceUID ReferencedSeriesInstanceUID_landmarks_SR.dcm sr_gt PatientID StudyInstanceUID ReferencedSeriesInstanceUID_features_SR.dcm For NLST, the '3d_fullres-tta' model was evaluated. The data is organized the same as above, where within each folder the PatientID and the StudyInstanceUID are subdirectories. For the NLST collection, it is possible that some patients have more than one StudyInstanceUID subdirectory. A separate directory for the DICOM SR body par regions ('sr_regions') and landmarks ('sr_landmarks') are also provided. The DICOM Segmentation and SR objects are named according to the SeriesInstanceUID of the original CT files. nlst 3d_fullres-tta PatientID StudyInstanceUID ReferencedSeriesInstanceUID_SEG.dcm ReferencedSeriesInstanceUID_features_SR.dcm sr_regions PatientID StudyInstanceUID ReferencedSeriesInstanceUID_regions_SR.dcm sr_landmarks PatientID StudyInstanceUID ReferencedSeriesInstanceUID_landmarks_SR.dcm The query used for NSCLC-Radiomics is here, and a list of corresponding PatientIDs/StudyInstanceUIDs/SeriesInstanceUIDs here. The query used for NLST is here, and a list of corresponding PatientIDs/StudyInstanceUIDs/SeriesInstanceUIDs here.
description: This project has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l. This project has also been funded in whole or in part with Federal funds from the National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Grant number T32EB025823 for Training in Image-Guidance, Precision Diagnosis and Therapy. The following are GitHub links to the packages used -- nnU-Net: https://github.com/MIC-DKFZ/nnUNet, Body part regression: https://github.com/mic-dkfz/bodypartregression, Pyradiomics: https://github.com/AIM-Harvard/pyradiomics, Highdicom: https://github.com/herrmannlab/highdicom, DCMQI: https://github.com/QIICR/dcmqi, GitHub repo with our Colaboratory notebooks: https://github.com/ImagingDataCommons/ai_medima_misc/tree/main/nnunet/notebooks
description: {"references": ["[1] Fedorov A, Longabaugh WJ, Pot D, Clunie DA, Pieper S, Aerts HJ, Homeyer A, Lewis R, Akbarzadeh A, Bontempi D, Clifford W. NCI imaging data commons. Cancer research. 2021 Aug 8;81(16):4188.", "[2] Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods. 2021 Feb;18(2):203-11.", "[3] Van Griethuysen JJ, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RG, Fillion-Robin JC, Pieper S, Aerts HJ. Computational radiomics system to decode the radiographic phenotype. Cancer research. 2017 Nov 1;77(21):e104-7.", "[4] Schuhegger S. Body Part Regression for CT Images. arXiv preprint arXiv:2110.09148. 2021 Oct 18.", "[5] Aerts, H. J. W. L., Wee, L., Rios Velazquez, E., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., Hoebers, F., Rietbergen, M. M., Leemans, C. R., Dekker, A., Quackenbush, J., Gillies, R. J., Lambin, P. (2019). Data From NSCLC-Radiomics [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.PF0M9REI", "[6] Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature communications. 2014 Jun 3;5(1):1-9.", "[7] National Lung Screening Trial Research Team. (2013). Data from the National Lung Screening Trial (NLST) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.HMQ8-J677", "[8] National Lung Screening Trial Research Team. Reduced lung-cancer mortality with low-dose computed tomographic screening. New England Journal of Medicine. 2011 Aug 4;365(5):395-409.", "[9] Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. Journal of digital imaging. 2013 Dec;26(6):1045-57.", "[10] Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten JW, da Silva Santos LB, Bourne PE, Bouwman J. The FAIR Guiding Principles for scientific data management and stewardship. Scientific data. 2016 Mar 15;3(1):1-9.", "[11] Herz C, Fillion-Robin JC, Onken M, Riesmeier J, Lasso A, Pinter C, Fichtinger G, Pieper S, Clunie D, Kikinis R, Fedorov A. DCMQI: an open source library for standardized communication of quantitative image analysis results using DICOM. Cancer research. 2017 Nov 1;77(21):e87-90.", "[12] Bridge CP, Gorman C, Pieper S, Doyle SW, Lennerz JK, Kalpathy-Cramer J, Clunie DA, Fedorov AY, Herrmann MD. Highdicom: A python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology. Journal of Digital Imaging. 2022 Aug 22:1-9."]}