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Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma
Open accessAbstract
Two CT features were developed to quantitatively describe lung adenocarcinomas by scoring tumor shape complexity (feature 1: convexity) and intratumor density variation (feature 2: entropy ratio) in routinely obtained diagnostic CT scans. The developed quantitative features were analyzed in two independent cohorts (cohort 1: n = 61; cohort 2: n = 47) of patients diagnosed with primary lung adenocarcinoma, retrospectively curated to include imaging and clinical data. Preoperative chest CTs were segmented semi-automatically. Segmented tumor regions were further subdivided into core and boundary sub-regions, to quantify intensity variations across the tumor. Reproducibility of the features was evaluated in an independent test-retest dataset of 32 patients. The proposed metrics showed high degree of reproducibility in a repeated experiment (concordance, CCC≥0.897; dynamic range, DR≥0.92). Association with overall survival was evaluated by Cox proportional hazard regression, Kaplan-Meier survival curves, and the log-rank test. Both features were associated with overall survival (convexity: p = 0.008; entropy ratio: p = 0.04) in Cohort 1 but not in Cohort 2 (convexity: p = 0.7; entropy ratio: p = 0.8). In both cohorts, these features were found to be descriptive and demonstrated the link between imaging characteristics and patient survival in lung adenocarcinoma.
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“Grove et al found these measures to be strong prognostic indicators in patients with early-stage lung cancer (P < .01) (56).”
From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [56]
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Cites
Siegel, R., Naishadham, D., & Jemal, A. (2013) · CA A Cancer Journal for Clinicians · doi:10.3322/caac.21166 · Cited by 11,825
A concordance correlation coefficient to evaluate reproducibility.
Lin, L. I.-K. (1989) · Biometrics · doi:10.2307/2532051 · Cited by 8,785
Radiomics: extracting more information from medical images using advanced feature analysis
Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R. G. P. M., Granton, P., Zegers, C. M. L., Gillies, R., Boellard, R., Dekker, A., et al. (2012) · European Journal of Cancer · doi:10.1016/j.ejca.2011.11.036 · Cited by 6,084
Self-citationThe quickhull algorithm for convex hulls
Barber, C. B., Dobkin, D., & Huhdanpaa, H. T. (1996) · ACM Transactions on Mathematical Software · doi:10.1145/235815.235821 · Cited by 5,562
Somatic mutations affect key pathways in lung adenocarcinoma
Ding, L., Getz, G., Wheeler, D. A., Mardis, E. R., McLellan, M. D., Cibulskis, K., Sougnez, C., Greulich, H., Muzny, D. M., Morgan, M. B., et al. (2008) · Nature · doi:10.1038/nature07423 · Cited by 2,681
Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer
Peifer, M., Fernandez‐Cuesta, L., Sos, M. L., George, J., Seidel, D., Kasper, L. H., Plenker, D., Leenders, F., Sun, R., Zander, T., et al. (2012) · Nature Genetics · doi:10.1038/ng.2396 · Cited by 1,404
Wojnar, L. (1998) · Cited by 912
Decoding global gene expression programs in liver cancer by noninvasive imaging
Segal, E., Sirlin, C. B., Ooi, C., Adler, A. S., Gollub, J., Chen, X., Chan, B. K., Matcuk, G. R., Barry, C. T., Chang, H. Y., et al. (2007) · Nature Biotechnology · doi:10.1038/nbt1306 · Cited by 629
Quantitative Imaging in Cancer Evolution and Ecology
Gatenby, R. A., Grove, O., & Gillies, R. J. (2013) · Radiology · doi:10.1148/radiol.13122697 · Cited by 515
Self-citationGaneshan, B., Panayiotou, E., Burnand, K., Dizdarevic, S., & Miles, K. A. (2011) · European Radiology · doi:10.1007/s00330-011-2319-8 · Cited by 504
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Sugawara, H., Kikkawa, N., Ito, K., Watanabe, H., Kaku, S., Akai, H., Abe, O., WATANABE, S.-I. I., Yatabe, Y., & Kusumoto, M. (2023) · British Journal of Radiology · doi:10.1093/bjr/tqad048 · Cited by 2
Saccenti, L., Varble, N., Borde, T., Mikhail, A. S., Kassin, M. T., Levy, E. B., Xu, S., Hazen, L., Ukeh, I. N., Vasco, C., et al. (2024) · La radiologia medica · doi:10.1007/s11547-024-01937-1 · Cited by 2
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Paolo, D., Greco, C., Ippolito, E., Fiore, M., Ramella, S., Soda, P., Tortora, M., Bria, A., & Sicilia, R. (2026) · Health Information Science and Systems · doi:10.1007/s13755-025-00404-z · Cited by 3
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Yin, X., Gao, M., Wang, W., & Zhang, Y. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/2703350 · Cited by 2
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Balagurunathan, Y., Choi, J. W., Thompson, Z., Jain, M. D., & Locke, F. L. (2025) · Cancers · doi:10.3390/cancers17111832 · Cited by 2
Self-citationUniversal Medical Imaging Model for Domain Generalization with Data Privacy
Radwan, A., Osman, I. I., & Shehata, M. (2025) · Intelligent Methods, Systems, and Applications (IMSA) · doi:10.1109/imsa65733.2025.11167392 · Cited by 2
Cao, X., Li, X., Wang, X., Duan, J., Zhu, S., Zeng, H., Yin, Y., Yuan, S., & Hu, X. (2019) · Dose-Response · doi:10.1177/1559325819897175 · Cited by 2
Elshafeey, N. A., Hassan, I. S. A., Zinn, P. O., & Colen, R. R. (2017) · Topics in Magnetic Resonance Imaging · doi:10.1097/rmr.0000000000000114 · Cited by 2
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Edwards, S. N., Brown, S. D., & Lee, M. (2018) · Journal of Biomedical Engineering and Medical Imaging · doi:10.14738/jbemi.52.4204 · Cited by 2
Enhancement Technique for Early Stage Lung Cancer Detection Using Foldscope Methodology
Jadhav, V. D., & Patil, L. V. (2021) · Springer eBooks · doi:10.1007/978-3-030-69921-5_19 · Cited by 2
Tian, J., Dong, D., Liu, Z., & Wei, J. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-818101-0.00004-5 · Cited by 3
Volumetric medical image compression using inter‐slice correlation switched prediction approach
Sharma, U., Sood, M., & Puthooran, E. (2020) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22425 · Cited by 2
Hasib, A., Islam, M., & Rab, R. (2020) · International Journal of Computer Applications · doi:10.5120/ijca2020920409 · Cited by 2
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Zhu, J.-N., Veeraraghavan, H., Jiang, J., Oh, J., Norton, L., Deasy, J., & Tannenbaum, A. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3325295 · Cited by 2
Dai, F., Mao, C., Guo, G., Mei, L., Feng, Y., Hao, Z., Wang, X., Zhang, L., & Deng, B. (2025) · BMC Cancer · doi:10.1186/s12885-025-14560-8 · Cited by 1
Chen, A. W., Torrez, C. R. Z., Peterson, L. M., Muzi, M., Specht, J. M., Cohen, E. A., Linden, H. M., Despina Kontos, B., & Mankoff, D. A. (2025) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-025-07570-y · Cited by 1
Rostami, S., Guérendel, C., Soliman, M., Stutterheim, H. W., Maxouri, O., Sánchez, D. I. R., Ursprung, S., Boveradze, N., Agrotis, G., Chupetlovska, K., et al. (2026) · European Radiology Experimental · doi:10.1186/s41747-026-00691-5 · Cited by 1
LobePrior segments lung lobes on computed tomography images in the presence of severe abnormalities
Ribeiro, J. A., do Carmo, D. S., Reis, F., Magalhães, R. S., Dertkigil, S. S. J., Appenzeller, S., & Rittner, L. (2026) · Scientific Reports · doi:10.1038/s41598-026-48136-8 · Cited by 1
Hamami, R., Zermi, N., Boubchir, L., & Khaldi, A. (2026) · Results in Control and Optimization · doi:10.1016/j.rico.2026.100750 · Cited by 1
3D pulmonary nodules detection using fast marching segmentation
Paing, M. P., & Choomchuay, S. (2018) · Journal of Fundamental and Applied Sciences · doi:10.4314/jfas.v9i4s.19 · Cited by 1
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Baby, J., Rajalakshmi, T., & Umapathy, S. (2019) · Lecture notes in computational vision and biomechanics · doi:10.1007/978-3-030-00665-5_101 · Cited by 1
Bianconi, F., Fravolini, M. L., Pascoletti, G., Palumbo, I., Scialpi, M., Aristei, C., & Palumbo, B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-12053-4_56 · Cited by 1
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Sharma, U., Sood, M., Puthooran, E., & Kumar, Y. (2022) · IGI Global eBooks · doi:10.4018/978-1-6684-7544-7.ch012 · Cited by 1
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Sweetline, B. C., & Vijayakumaran, C. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-3250-4_8 · Cited by 1
Sparse-view X-ray CT based on a box-constrained nonlinear weighted anisotropic TV regularization
Li, H., & Song, Y. (2024) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2024223 · Cited by 1
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Kandathil, A., Kay, F. U., Batra, K., Saboo, S. S., & Rajiah, P. S. (2018) · Seminars in Roentgenology · doi:10.1053/j.ro.2018.02.006 · Cited by 1
Statistical survey of open source medical image databases on the Internet
Wang, H., Ma, X., Zhai, H., Liao, Y., Wu, Y., Chen, N., Zhang, S., & Zhang, B. (2019) · Digital Medicine · doi:10.4103/digm.digm_1_19 · Cited by 1
Self-citationDemir, C. (2021) · Medical Science and Discovery · doi:10.36472/msd.v8i7.578 · Cited by 1
Mahmood, U. A., Apte, A., Kanan, C., Bates, . D. B., Corrias, G., Manneli, L., Oh, J. H., Erdi, Y. E., Nguyen, J., Deasy, J. O., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/773879 · Cited by 1
Radiation Therapy for Liver Tumors: Future Directions
Mellon, E. A., Murimwa, G. Z., & Hoffe, S. E. (2017) · Springer eBooks · doi:10.1007/978-3-319-54531-8_20
Fave, X. J. (2017) · Digital Commons-TMC (Texas Medical Center)
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Ghani, T., & Oommen, B. J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-64984-5_4
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Rahul, N., Manjunath, K. N., Manuel, M., & Kurady, R. (2020) · IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) · doi:10.1109/discover50404.2020.9278052
Treatment evaluation and prognosis prediction using radiomics in clinical practice
Tian, J., Dong, D., Liu, Z., & Wei, J. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-818101-0.00002-1
Radiomics and radiogenomics in lung cancer – a short review
Semenescu, L. E., Crișan, A. E., Mireștean, C. C., Mitrea, A., Iancu, I. R., & Iancu, D. P. T. (2021) · Oncolog-Hematolog ro · doi:10.26416/onhe.5.1.2021.4573
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吐尔孙阿依, ·. (2023) · Advances in Clinical Medicine · doi:10.12677/acm.2023.13122653
Technological evolution in cancer diagnosis and therapeutics
Mishra, M., Tyagi, B., Taimoor, N., & Bose, S. K. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95116-6.00018-9
Sun, C. Q., Liu, Y., & Yang, H. (2024) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2023edl8049
Cao, C., Zhang, J.-H., Gao, Y., & Li, Z. (2024) · Asian Conference on Computer Vision · doi:10.1007/978-981-96-0901-7_1
Comparing Inclusion Methods on Juxta-pleural into Lung Parenchyma
Gunawan, R., Tran, Y., Zheng, J., Nguyen, H. T., Carrigan, A., Mills, M. K., & Chai, R. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10782719
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Kumar, Y., Singh, P. K., & Yadav, G. (2025) · Biomedical Engineering Applications Basis and Communications · doi:10.4015/s1016237225500061
Bravo, R., Robertson‐Tessi, M., Antonia, S. J., Gray, J. E., Beg, A. A., Gatenby, R. A., Schabath, M. B., & Anderson, A. R. A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.25.676637
Self-citationAdditional file 1 of Leveraging hybrid biomarkers in clinical endpoint prediction
Saad, M., & Lee, I. H. (2020) · Figshare · doi:10.6084/m9.figshare.13065770
Additional file 1 of Are radiomics features universally applicable to different organs?
Lee, S.-H., Cho, H.-H., Kwon, J., Lee, H. Y., & Park, H. (2021) · Figshare · doi:10.6084/m9.figshare.14385475
Liu, Q., Huang, Y., Chen, H., Liu, Y., Liang, R., & Zeng, Q. (2020) · Figshare · doi:10.6084/m9.figshare.12450272
Invernizzi, C., Benveniste, P., Ayachy, R. E., Leroy, T., Blanchard, N., Schipman, B., Debelleix, C., Chamois, J., Pointreau, Y., & Bibault, J. (2026) · Research Square · doi:10.21203/rs.3.rs-8743399/v1
Imagerie quantitative fonctionnelle et radiomique des masses orbitaires
Lecler, A. (2018) · theses.fr (ABES) · doi:10.70675/453435aazc87bz4f6cz9f70zb8afa34a843e
Radiomics of metastatic cell-clear renal cancer
Bouchouicha, A. (2018) · doi:10.70675/3a5aaffdz3530z4603z8f41zb379a525d0da
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Altinok, O., Ho, W. L. J., Robinson, L. A., Goldgof, D. B., Hall, L., Güveniş, A., & Schabath, M. B. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6694630
Self-citationHamami, R., Zermi, N., Boubchir, L., & Khaldi, A. (2026) · Optics & Laser Technology · doi:10.1016/j.optlastec.2026.115709
Sofos, F. D., Pana, Z. D., & Drikakis, D. (2026) · PLoS ONE · doi:10.1371/journal.pone.0351081
Zeng, G., Li, W., Li, X., Sun, R., Lam, S., Xiong, T., Cheung, H.-Y., Lee, V. H.-F., Lee, K.-H., Cheung, L.-Y., et al. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1859909
Metadata
- Authors
- Grove, O., Berglund, A. E., Schabath, M. B., Aerts, H. J. W. L., Dekker, A., Wang, H., Velazquez, E. R., Lambin, P., Gu, Y., Balagurunathan, Y., Eikman, E., Gatenby, R. A., Eschrich, S., & Gillies, R. J.
- Affiliations
- Moffitt Cancer Center (US)
- Brigham and Women's Hospital (US)
- Maastricht University (NL)
- Tianjin Medical University Cancer Institute and Hospital (CN)
- Corresponding author
- Gillies, Robert J.
- ORCID
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-03-04
- Volume
- 10
- Issue
- 3
- Pages
- e0118261
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4349806
- OpenAlex
- W2067959276
- Cited by
- 263
- References
- 30
- Citations by year
- 2026: 9 · 2025: 12 · 2024: 16 · 2023: 19 · 2022: 18 · 2021: 32 · 2020: 38 · 2019: 32 · 2018: 34 · 2017: 27 · 2016: 22 · 2015: 4
- FWCI
- 23.50
- Type
- journal article
- Language
- en
- Update notices
- correction (2021-03-08)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion, cc-by)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Funders
- National Institutes of Health (NIH/NCI P50CA119997)
- National Cancer Institute (U01CA143062, P50CA119997, U01CA196405, P30CA076292, R25CA174664)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Keywords
- lung adenocarcinoma, intratumoral heterogeneity, CT imaging features, prognostic factors, overall survival, tumor segmentation, reproducibility
- MeSH terms
- Adenocarcinoma of Lung, Adenocarcinoma, Aged, Female, Humans, Image Processing, Computer-Assisted, Lung Neoplasms, Male, Middle Aged, Prognosis, Retrospective Studies, Tomography, X-Ray Computed, Proportional Hazards Models, Entropy
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