ArticleInternational journal of radiation oncology, biology, physics2016
Robust Intratumor Partitioning to Identify High-Risk Subregions in Lung Cancer: A Pilot Study.
Article in International journal of radiation oncology, biology, physics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 3 of them syntheses that pooled it.
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Who cites it
44 citing papers in PubMed, 3 syntheses or guidelines pooled it, 90 citations in OpenAlex.
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- Clinical applications of textural analysis in non-small cell lung cancer.The British journal of radiology · 2018Pooled it
- Pretreatment metabolic tumour volume in stage IIIA/B non-small-cell lung cancer uncovers differences in effectiveness of definitive radiochemotherapy schedules: analysis of the ESPATUE randomized phase 3 trial.European journal of nuclear medicine and molecular imaging · 2019Trial
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- Multi-institutional development and validation of habitat imaging for predicting outcomes of first-line immunotherapy in advanced non-small cell lung cancer.Translational lung cancer research · 2025Article
- Progress and challenges of artificial intelligence in lung cancer clinical translation.NPJ precision oncology · 2025Review
- Spatial-temporal radiogenomics in predicting neoadjuvant chemotherapy efficacy for breast cancer: a comprehensive review.Journal of translational medicine · 2025Review
- Prediction of induction chemotherapy efficacy in patients with locally advanced nasopharyngeal carcinoma using habitat subregions derived from multi-modal MRI radiomics.Frontiers in oncology · 2025Article
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- Using tumor habitat-derived radiomic analysis during pretreatmentCancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Subregion-specificAmerican journal of nuclear medicine and molecular imaging · 2024Article
- Recent Advances in Deep Learning and Medical Imaging for Head and Neck Cancer Treatment: MRI, CT, and PET Scans.Cancers · 2023Review
- Application of radiomics in lung immuno-oncology.Precision radiation oncology · 2023Review
- Habitat Imaging Biomarkers for Diagnosis and Prognosis in Cancer Patients Infected with COVID-19.Cancers · 2022Article
- Voxel-wise supervised analysis of tumors with multimodal engineered features to highlight interpretable biological patterns.Medical physics · 2022Article
- RADIomic Spatial TexturAl Descriptor (RADISTAT): Quantifying Spatial Organization of Imaging Heterogeneity Associated With Tumor Response to Treatment.IEEE journal of biomedical and health informatics · 2022Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
purposeTo develop an intratumor partitioning framework for identifying high-risk subregions from (18)F-fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) imaging and to test whether tumor burden associated with the high-risk subregions is prognostic of outcomes in lung cancer. METHODS AND MATERIALS: In this institutional review board-approved retrospective study, we analyzed the pretreatment FDG-PET and CT scans of 44 lung cancer patients treated with radiation therapy. A novel, intratumor partitioning method was developed, based on a 2-stage clustering process: first at the patient level, each tumor was over-segmented into many superpixels by k-means clustering of integrated PET and CT images; next, tumor subregions were identified by merging previously defined superpixels via population-level hierarchical clustering. The volume associated with each of the subregions was evaluated using Kaplan-Meier analysis regarding its prognostic capability in predicting overall survival (OS) and out-of-field progression (OFP).
resultsThree spatially distinct subregions were identified within each tumor that were highly robust to uncertainty in PET/CT co-registration. Among these, the volume of the most metabolically active and metabolically heterogeneous solid component of the tumor was predictive of OS and OFP on the entire cohort, with a concordance index or CI of 0.66-0.67. When restricting the analysis to patients with stage III disease (n=32), the same subregion achieved an even higher CI of 0.75 (hazard ratio 3.93, log-rank P=.002) for predicting OS, and a CI of 0.76 (hazard ratio 4.84, log-rank P=.002) for predicting OFP. In comparison, conventional imaging markers, including tumor volume, maximum standardized uptake value, and metabolic tumor volume using threshold of 50% standardized uptake value maximum, were not predictive of OS or OFP, with CI mostly below 0.60 (log-rank P>.05).
conclusionWe propose a robust intratumor partitioning method to identify clinically relevant, high-risk subregions in lung cancer. We envision that this approach will be applicable to identifying useful imaging biomarkers in many cancer types.
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