Radiomic Features Prognosticate Treatment Response in CAR-T Cell Therapy
Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Patient Data
2.2. Metabolic Tumor Volume
2.3. Radiological Review
2.4. Radiomics
2.5. Statistical Analysis
3. Results
4. Discussion
5. Limitations
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
PC | Principal components |
CT | Computed tomography |
PET | Positron emission tomography |
CAR-T | Chimeric antigen receptor T-cell therapy |
Axi-cel | Axicabtagene ciloleucel |
LDH | Lactate Dehydrogenase |
ECOG | Eastern Cooperative Oncology Group |
MTV | Metabolic tumor volume |
References
- Kulkarni, N.M.; Pinho, D.F.; Narayanan, S.; Kambadakone, A.R.; Abramson, J.S.; Sahani, D.V. Imaging for Oncologic Response Assessment in Lymphoma. Am. J. Roentgenol. 2016, 208, 18–31. [Google Scholar] [CrossRef] [PubMed]
- Paquin, A.R.; Oyogoa, E.; McMurry, H.S.; Kartika, T.; West, M.; Shatzel, J.J. The diagnosis and management of suspected lymphoma in general practice. Eur. J. Haematol. 2023, 110, 3–13. [Google Scholar] [CrossRef] [PubMed]
- A clinical evaluation of the International Lymphoma Study Group classification of non-Hodgkin’s lymphoma. The Non-Hodgkin’s Lymphoma Classification Project. Blood 1997, 89, 3909–3918. [Google Scholar]
- Metzger, M.L.; Mauz-Körholz, C. Epidemiology, outcome, targeted agents and immunotherapy in adolescent and young adult non-Hodgkin and Hodgkin lymphoma. Br. J. Haematol. 2019, 185, 1142–1157. [Google Scholar] [CrossRef]
- Susanibar-Adaniya, S.; Barta, S.K. 2021 Update on Diffuse large B cell lymphoma: A review of current data and potential applications on risk stratification and management. Am. J. Hematol. 2021, 96, 617–629. [Google Scholar] [CrossRef]
- Locke, F.L.; Neelapu, S.S.; Bartlett, N.L.; Siddiqi, T.; Chavez, J.C.; Hosing, C.M.; Ghobadi, A.; Budde, L.E.; Bot, A.; Rossi, J.M.; et al. Phase 1 Results of ZUMA-1: A Multicenter Study of KTE-C19 Anti-CD19 CAR T Cell Therapy in Refractory Aggressive Lymphoma. Mol. Ther. 2017, 25, 285–295. [Google Scholar] [CrossRef]
- Neelapu, S.S.; Locke, F.L.; Bartlett, N.L.; Lekakis, L.J.; Miklos, D.B.; Jacobson, C.A.; Braunschweig, I.; Oluwole, O.O.; Siddiqi, T.; Lin, Y.; et al. Axicabtagene Ciloleucel CAR T-Cell Therapy in Refractory Large B-Cell Lymphoma. N. Engl. J. Med. 2017, 377, 2531–2544. [Google Scholar] [CrossRef]
- Brudno, J.N.; Kochenderfer, J.N. Recent advances in CAR T-cell toxicity: Mechanisms, manifestations and management. Blood Rev. 2019, 34, 45–55. [Google Scholar] [CrossRef]
- Maurer, M.J. The International Prognostic Index in aggressive B-cell lymphoma. Haematologica 2023, 108, 2874–2879. [Google Scholar] [CrossRef]
- International Non-Hodgkin’s Lymphoma Prognostic Factors Project. A predictive model for aggressive non-Hodgkin’s lymphoma. N. Engl. J. Med. 1993, 329, 987–994. [Google Scholar] [CrossRef]
- Sehn, L.H.; Berry, B.; Chhanabhai, M.; Fitzgerald, C.; Gill, K.; Hoskins, P.; Klasa, R.; Savage, K.J.; Shenkier, T.; Sutherland, J.; et al. The revised International Prognostic Index (R-IPI) is a better predictor of outcome than the standard IPI for patients with diffuse large B-cell lymphoma treated with R-CHOP. Blood 2007, 109, 1857–1861. [Google Scholar] [CrossRef] [PubMed]
- Cheson, B.D.; Fisher, R.I.; Barrington, S.F.; Cavalli, F.; Schwartz, L.H.; Zucca, E.; Lister, T.A. Recommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: The Lugano classification. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 2014, 32, 3059–3068. [Google Scholar] [CrossRef] [PubMed]
- Johnson, S.A.; Kumar, A.; Matasar, M.J.; Schöder, H.; Rademaker, J. Imaging for Staging and Response Assessment in Lymphoma. Radiology 2015, 276, 323–338. [Google Scholar] [CrossRef]
- Dean, E.A.; Mhaskar, R.S.; Lu, H.; Mousa, M.S.; Krivenko, G.S.; Lazaryan, A.; Bachmeier, C.A.; Chavez, J.C.; Nishihori, T.; Davila, M.L.; et al. High metabolic tumor volume is associated with decreased efficacy of axicabtagene ciloleucel in large B-cell lymphoma. Blood Adv. 2020, 4, 3268–3276. [Google Scholar] [CrossRef]
- Vercellino, L.; Cottereau, A.S.; Casasnovas, O.; Tilly, H.; Feugier, P.; Chartier, L.; Fruchart, C.; Roulin, L.; Oberic, L.; Pica, G.M.; et al. High total metabolic tumor volume at baseline predicts survival independent of response to therapy. Blood 2020, 135, 1396–1405. [Google Scholar] [CrossRef] [PubMed]
- Voorhees, T.J.; Zhao, B.; Oldan, J.; Hucks, G.; Khandani, A.; Dittus, C.; Smith, J.; Morrison, J.K.; Cheng, C.J.; Ivanova, A.; et al. Pretherapy metabolic tumor volume is associated with response to CD30 CAR T cells in Hodgkin lymphoma. Blood Adv. 2022, 6, 1255–1263. [Google Scholar] [CrossRef]
- Caimi, P.F.; Hill, B.T.; Hsi, E.D.; Smith, M.R. Clinical approach to diffuse large B cell lymphoma. Blood Rev. 2016, 30, 477–491. [Google Scholar] [CrossRef]
- Aerts, H.J.W.L.; Velazquez, E.R.; Leijenaar, R.T.H.; Parmar, C.; Grossmann, P.; Carvalho, S.; Bussink, J.; Monshouwer, R.; Haibe-Kains, B.; Rietveld, D.; et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 2014, 5, 4006. [Google Scholar] [CrossRef]
- Grossmann, P.; Stringfield, O.; El-Hachem, N.; Bui, M.M.; Rios Velazquez, E.; Parmar, C.; Leijenaar, R.T.; Haibe-Kains, B.; Lambin, P.; Gillies, R.J.; et al. Defining the biological basis of radiomic phenotypes in lung cancer. Elife 2017, 6, e23421. [Google Scholar] [CrossRef]
- Kumar, V.; Gu, Y.; Basu, S.; Berglund, A.; Eschrich, S.A.; Schabath, M.B.; Forster, K.; Aerts, H.J.; Dekker, A.; Fenstermacher, D.; et al. Radiomics: The process and the challenges. Magn. Reson. Imaging 2012, 30, 1234–1248. [Google Scholar] [CrossRef]
- Ligero, M.; Simó, M.; Carpio, C.; Iacoboni, G.; Balaguer-Montero, M.; Navarro, V.; Sánchez-Salinas, M.A.; Bobillo, S.; Marín-Niebla, A.; Iraola-Truchuelo, J.; et al. PET-based radiomics signature can predict durable responses to CAR T-cell therapy in patients with large B-cell lymphoma. eJHaem 2023, 4, 1081–1088. [Google Scholar] [CrossRef]
- Balagurunathan, Y.; Wei, Z.; Qi, J.; Thompson, Z.; Dean, E.; Lu, H.; Vardhanabhuti, S.; Corallo, S.; Choi, J.W.; Kim, J.J.; et al. Radiomic Features on PET/CT Imaging of Large B cell Lymphoma Lesions Predicts CAR T-cell Therapy Efficacy. Front. Oncol. Sect. Hematol. Malig. 2024, 14, 1485039. [Google Scholar] [CrossRef]
- O, J.H.; Lodge, M.A.; Wahl, R.L. Practical PERCIST: A Simplified Guide to PET Response Criteria in Solid Tumors 1.0. Radiology 2016, 280, 576–584. [Google Scholar] [CrossRef] [PubMed]
- Moghbel, M.C.; Kostakoglu, L.; Zukotynski, K.; Chen, D.L.; Nadel, H.; Niederkohr, R.; Mittra, E. Response Assessment Criteria and Their Applications in Lymphoma: Part 1. J. Nucl. Med. 2016, 57, 928–935. [Google Scholar] [CrossRef]
- Balagurunathan, Y.; Gu, Y.; Wang, H.; Kumar, V.; Grove, O.; Hawkins, S.; Kim, J.; Goldgof, D.B.; Hall, L.O.; Gatenby, R.A.; et al. Reproducibility and Prognosis of Quantitative Features Extracted from CT Images. Transl. Oncol. 2014, 7, 72–87. [Google Scholar] [CrossRef] [PubMed]
- Balagurunathan, Y.; Kumar, V.; Gu, Y.; Kim, J.; Wang, H.; Liu, Y.; Goldgof, D.B.; Hall, L.O.; Korn, R.; Zhao, B.; et al. Test-retest reproducibility analysis of lung CT image features. J. Digit. Imaging 2014, 27, 805–823. [Google Scholar] [CrossRef] [PubMed]
- Zwanenburg, A.; Vallières, M.; Abdalah, M.A.; Aerts, H.; Andrearczyk, V.; Apte, A.; Ashrafinia, S.; Bakas, S.; Beukinga, R.J.; Boellaard, R.; et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology 2020, 295, 328–338. [Google Scholar] [CrossRef]
- Bland, J.M.; Altman, D.G. The logrank test. BMJ Clin. Res. Ed. 2004, 328, 1073. [Google Scholar] [CrossRef]
- Cox, D.R. Regression models and life-tables. J. R. Stat. Soc. Ser. B Methodol. 1972, 34, 187–220. [Google Scholar] [CrossRef]
- Uno, H.; Cai, T.; Pencina, M.J.; D’Agostino, R.B.; Wei, L.J. On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Stat. Med. 2011, 30, 1105–1117. [Google Scholar] [CrossRef]
- Storey, J.D.; Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA 2003, 100, 9440–9445. [Google Scholar] [CrossRef] [PubMed]
- Campo, E.; Swerdlow, S.H.; Harris, N.L.; Pileri, S.; Stein, H.; Jaffe, E.S. The 2008 WHO classification of lymphoid neoplasms and beyond: Evolving concepts and practical applications. Blood 2011, 117, 5019–5032. [Google Scholar] [CrossRef]
- Arber, D.A.; Orazi, A.; Hasserjian, R.; Thiele, J.; Borowitz, M.J.; Le Beau, M.M.; Bloomfield, C.D.; Cazzola, M.; Vardiman, J.W. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood 2016, 127, 2391–2405. [Google Scholar] [CrossRef] [PubMed]
- Leithner, D.; Flynn, J.R.; Devlin, S.M.; Mauguen, A.; Fei, T.; Zeng, S.; Zheng, J.; Imber, B.S.; Hubbeling, H.; Mayerhoefer, M.E.; et al. Conventional and novel [(18)F]FDG PET/CT features as predictors of CAR-T cell therapy outcome in large B-cell lymphoma. J. Hematol. Oncol. 2024, 17, 21. [Google Scholar] [CrossRef]
- Feeney, J.; Horwitz, S.; Gönen, M.; Schöder, H. Characterization of T-cell lymphomas by FDG PET/CT. AJR. Am. J. Roentgenol. 2010, 195, 333–340. [Google Scholar] [CrossRef]
- Wang, S.; Chen, A.; Yang, L.; Cai, L.; Xie, Y.; Fujimoto, J.; Gazdar, A.; Xiao, G. Comprehensive analysis of lung cancer pathology images to discover tumor shape and boundary features that predict survival outcome. Sci. Rep. 2018, 8, 10393. [Google Scholar] [CrossRef]
- Grove, O.; Berglund, A.E.; Schabath, M.B.; Aerts, H.J.; Dekker, A.; Wang, H.; Velazquez, E.R.; Lambin, P.; Gu, Y.; Balagurunathan, Y.; et al. Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. PLoS ONE 2015, 10, e0118261. [Google Scholar] [CrossRef] [PubMed]
- Palumbo, B.; Bianconi, F.; Palumbo, I.; Fravolini, M.L.; Minestrini, M.; Nuvoli, S.; Stazza, M.L.; Rondini, M.; Spanu, A. Value of Shape and Texture Features from 18F-FDG PET/CT to Discriminate between Benign and Malignant Solitary Pulmonary Nodules: An Experimental Evaluation. Diagnostics 2020, 10, 696. [Google Scholar] [CrossRef]
- Cheson, B.D.; Pfistner, B.; Juweid, M.E.; Gascoyne, R.D.; Specht, L.; Horning, S.J.; Coiffier, B.; Fisher, R.I.; Hagenbeek, A.; Zucca, E.; et al. Revised response criteria for malignant lymphoma. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 2007, 25, 579–586. [Google Scholar] [CrossRef]
- Ather, S.; Kadir, T.; Gleeson, F. Artificial intelligence and radiomics in pulmonary nodule management: Current status and future applications. Clin. Radiol. 2020, 75, 13–19. [Google Scholar] [CrossRef]
- Balagurunathan, Y.; Schabath, M.B.; Wang, H.; Liu, Y.; Gillies, R.J. Quantitative Imaging features Improve Discrimination of Malignancy in Pulmonary nodules. Sci. Rep. 2019, 9, 8528. [Google Scholar] [CrossRef]
- Napel, S.; Giger, M. Special Section Guest Editorial:Radiomics and Imaging Genomics: Quantitative Imaging for Precision Medicine. J. Med. Imaging 2015, 2, 041001. [Google Scholar] [CrossRef] [PubMed]
- Zhou, M.; Scott, J.; Chaudhury, B.; Hall, L.; Goldgof, D.; Yeom, K.W.; Iv, M.; Ou, Y.; Kalpathy-Cramer, J.; Napel, S.; et al. Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches. AJNR Am. J. Neuroradiol. 2018, 39, 208–216. [Google Scholar] [CrossRef]
- Levstek, L.; Janžič, L.; Ihan, A.; Kopitar, A.N. Biomarkers for prediction of CAR T therapy outcomes: Current and future perspectives. Front. Immunol. 2024, 15, 1378944. [Google Scholar] [CrossRef] [PubMed]
- Cottereau, A.S.; Meignan, M.; Nioche, C.; Capobianco, N.; Clerc, J.; Chartier, L.; Vercellino, L.; Casasnovas, O.; Thieblemont, C.; Buvat, I. Risk stratification in diffuse large B-cell lymphoma using lesion dissemination and metabolic tumor burden calculated from baseline PET/CT(†). Ann. Oncol. Off. J. Eur. Soc. Med. Oncol. 2021, 32, 404–411. [Google Scholar] [CrossRef]
- Hwang, S.H.; Jung, M.; Jeong, Y.H.; Jo, K.; Kim, S.; Wang, J.; Cho, A. Prognostic value of metabolic tumor volume and total lesion glycolysis on preoperative (18)F-FDG PET/CT in patients with localized primary gastrointestinal stromal tumors. Cancer Metab. 2021, 9, 8. [Google Scholar] [CrossRef]
- Ilyas, H.; Mikhaeel, N.G.; Dunn, J.T.; Rahman, F.; Møller, H.; Smith, D.; Barrington, S.F. Defining the optimal method for measuring baseline metabolic tumour volume in diffuse large B cell lymphoma. Eur. J. Nucl. Med. Mol. Imaging 2018, 45, 1142–1154. [Google Scholar] [CrossRef]
- Bobillo, S.; Joffe, E.; Lavery, J.A.; Sermer, D.; Ghione, P.; Noy, A.; Caron, P.C.; Hamilton, A.; Hamlin, P.A.; Horwitz, S.M.; et al. Clinical characteristics and outcomes of extranodal stage I diffuse large B-cell lymphoma in the rituximab era. Blood 2021, 137, 39–48. [Google Scholar] [CrossRef]
- Beyar Katz, O.; Perry, C.; Grisariu-Greenzaid, S.; Yehudai-Ofir, D.; Luttwak, E.; Avni, B.; Zuckerman, T.; Sdayoor, I.; Stepensky, P.; Ringelstein-Harlev, S.; et al. Response rates of extra-nodal diffuse large B cell lymphoma to anti-CD19-CAR T cells: A real word retrospective multicenter study. Eur. J. Haematol. 2023, 111, 63–71. [Google Scholar] [CrossRef]
- Raskin, W.; Harle, I.; Hopman, W.M.; Booth, C.M. Prognosis, Treatment Benefit and Goals of Care: What do Oncologists Discuss with Patients who have Incurable Cancer? Clin. Oncol. R. Coll. Radiol. 2016, 28, 209–214. [Google Scholar] [CrossRef] [PubMed]
- Ethier, J.L.; Paramsothy, T.; You, J.J.; Fowler, R.; Gandhi, S. Perceived Barriers to Goals of Care Discussions With Patients With Advanced Cancer and Their Families in the Ambulatory Setting: A Multicenter Survey of Oncologists. J. Palliat. Care 2018, 33, 125–142. [Google Scholar] [CrossRef] [PubMed]
- Jalaguier-Coudray, A.; Thomassin-Piana, J. Solid masses: What are the underlying histopathological lesions? Diagn. Interv. Imaging 2014, 95, 153–168. [Google Scholar] [CrossRef] [PubMed]
- Zhang, J.; Li, Y.; Zhao, Y.; Qiao, J. CT and MRI of superficial solid tumors. Quant. Imaging Med. Surg. 2018, 8, 232–251. [Google Scholar] [CrossRef] [PubMed]
- Senjo, H.; Hirata, K.; Izumiyama, K.; Minauchi, K.; Tsukamoto, E.; Itoh, K.; Kanaya, M.; Mori, A.; Ota, S.; Hashimoto, D.; et al. High metabolic heterogeneity on baseline 18FDG-PET/CT scan as a poor prognostic factor for newly diagnosed diffuse large B-cell lymphoma. Blood Adv. 2020, 4, 2286–2296. [Google Scholar] [CrossRef]
- Stella, F.; Chiappella, A.; Casadei, B.; Bramanti, S.; Ljevar, S.; Chiusolo, P.; Di Rocco, A.; Tisi, M.C.; Carrabba, M.G.; Cutini, I.; et al. A Multicenter Real-life Prospective Study of Axicabtagene Ciloleucel versus Tisagenlecleucel Toxicity and Outcomes in Large B-cell Lymphomas. Blood Cancer Discov. 2024, 5, 318–330. [Google Scholar] [CrossRef]
- Zhang, X.; Yang, X.; Wang, C.; Huang, L.; Zhang, Y.; Wei, J. High Expression of Plasma IL-1β Levels and Transition of Regulatory T-Cell Subsets Correlate with Disease Progression in Myelodysplastic Syndrome. Blood 2022, 140, 9761–9762. [Google Scholar] [CrossRef]
- Peng, J.; Yang, X.; Li, C.; Zhang, Y.; Ma, L.; Wei, J. High Serum ST2 Level Predicts Progression and Poor Prognosis of Denovomyelodysplastic Syndrome. Blood 2023, 142, 6455. [Google Scholar] [CrossRef]
- Gong, Y.; Fei, P.; Zhang, Y.; Xu, Y.; Wei, J. From Multi-Omics to Visualization and Beyond: Bridging Micro and Macro Insights in CAR-T Cell Therapy. Adv. Sci. 2025, e2501095. [Google Scholar] [CrossRef]
- Luciani, F.; Safavi, A.; Guruprasad, P.; Chen, L.; Ruella, M. Advancing CAR T-cell Therapies with Artificial Intelligence: Opportunities and Challenges. Blood Cancer Discov. 2025, 6, 159–162. [Google Scholar] [CrossRef]
- Shahzadi, M.; Rafique, H.; Waheed, A.; Naz, H.; Waheed, A.; Zokirova, F.R.; Khan, H. Artificial intelligence for chimeric antigen receptor-based therapies: A comprehensive review of current applications and future perspectives. Ther. Adv. Vaccines Immunother. 2024, 12. [Google Scholar] [CrossRef]
- Balagurunathan, Y.; Mitchell, R.; El Naqa, I. Requirements and reliability of AI in the medical context. Phys. Medica 2021, 83, 72–78. [Google Scholar] [CrossRef] [PubMed]
- Afroogh, S.; Akbari, A.; Malone, E.; Kargar, M.; Alambeigi, H. Trust in AI: Progress, challenges, and future directions. Humanit. Social. Sci. Commun. 2024, 11, 1568. [Google Scholar] [CrossRef]
Characteristics | All Patients * (N = 155) | Subcohorts (Lesion Level) | |
---|---|---|---|
Extra-Nodal (n = 94) |
Lymphatic (n = 124) | ||
Age (mean, median, std.dev) | 60.1 (63, 12.2) | 59.4 (63.5, 12.8) | 61 (63, 10.9) |
Sex (male/female/unavailable) | 61/39/55 | 36/22 | 53/29 |
LDH (mean, median, std.dev) | 400.5 (266, 348.25) | 448.3 (275.5, 406.79) | 408.6 (267.5, 353.4) |
ECOG | |||
0–1 | 83 | 48 | 66 |
2–3 | 17 | 10 | 16 |
One Year Progression | /death No Yes Unavailable 55 | 24 (41.4%) 34 (58.6%) | 38(46.3%) 44 (53.7%) |
Stage | |||
I/II III/IV | 22 78 | 10 48 | 14 68 |
Bridge Therapy | |||
Yes No | Yes: 50 No: 50 | Yes: 28 No: 30 | Yes: 41 No: 41 |
Unavailable | 55 | ||
Axi-cel therapy | |||
Trial (cancer center) Consortium (Zuma-1) | 100 55 | 58 36 | 82 42 |
Radiomic Metric (Principal Component, PC) | Spearman Correlation (ρ) | p-Value |
---|---|---|
Lymphatics (CT Images) | ||
Size PC1 | 0.368 | 0.0000268 |
Shape PC1 | 0.3002 | 0.0007 |
Texture PC1 | −0.345 | 0.00008 |
Extra-Nodal (CT Images) | ||
Size PC1 | −0.4519 | 0.000004 |
Shape PC1 | 0.3698 | 0.00024 |
Texture PC1 | 0.5535 | 0.00 |
Lymphatics (PET Images) | ||
Size PC1 | 0.3698 | 0.000023 |
Shape PC1 | 0.2717 | 0.0023 |
Texture PC1 | 0.175 | 0.0518 |
Extra-Nodal (PET Images) | ||
Size PC1 | 0.4496 | 0.0535 |
Shape PC1 | 0.3590 | 0.00038 |
Texture PC1 | −0.275 | 0.0073 |
Features | Loading Factors | Median Value | |||
---|---|---|---|---|---|
PC1 | PC2 | PC3 | MTV (Median): 169.94 mL | ||
Shape-Related Features | Shape-PC1 (Median): −0.1909 | ||||
1 | Compactness_1 | −0.431 | −0.122 | 0.024 | |
2 | Compactness_2 | −0.414 | −0.074 | 0.025 | |
3 | Spherical_disproportion | 0.405 | 0.224 | 0.017 | |
4 | Sphericity | −0.434 | −0.141 | 0.020 | |
5 | Asphericity | 0.405 | 0.224 | 0.017 | |
Size-Related Features | Size-PC1 (Median): −0.2013 | ||||
1 | SUV (volume_at_intensity_fraction_10) | −0.031 | −0.123 | −0.338 | |
2 | SUV (volume_at_intensity_fraction_90) | −0.147 | −0.032 | −0.149 | |
3 | SUV (intensity_at_volume_fraction_10) | −0.097 | −0.101 | −0.297 | |
4 | SUV (intensity_at_volume_fraction_90) | 0.023 | −0.129 | −0.331 | |
5 | SUV (volume_at_intensity_fraction_difference) | 0.011 | −0.121 | −0.316 | |
Texture-Related Features | Texture-PC1 (Median): −2.9435 | ||||
1 | SUV (avg_coocurrence_joint_max) | 0.030 | 0.001 | −0.078 | |
2 | SUV (avg_coocurrence_joint_average) | −0.012 | 0.054 | 0.003 | |
3 | SUV (avg_coocurrence_joint_variance) | 0.050 | −0.104 | 0.020 | |
4 | SUV (avg_coocurrence_joint_entropy) | −0.064 | 0.055 | 0.044 | |
5 | SUV (avg_coocurrence_difference_average) | 0.074 | −0.129 | 0.060 |
A. Metrics on Computed Tomography (CT) Imaging | |||||
Variable | Survival Statistics (Log-rank p-Value, q-Value) | ||||
OverAll Survival (OS) | Progression Free Survival (PFS) | ||||
Nodal (n = 126) | Extra Nodal (n = 94) | Nodal (n = 126) | Extra Nodal (n = 94) | ||
1 | MTV (total body) | <0.0001 * (0.0003) | <0.0001 * (0.0002) | 0.00024 * (0.0096) | 0.00017 * (0.00096) |
2 | Shape-PC1 | 0.75 (0.75) | 0.008 * (0.013) | 0.67 (0.72) | 0.017 * (0.0272) |
3 | Size-PC | 0.18 (0.24) | 0.0012 * (0.003) | 0.22 (0.293) | 0.00046 * (0.0012) |
4 | Texture-PC | 0.58 (0.663) | 0.0037 * (0.007) | 0.72 (0.72) | 0.0022 * (0.0044) |
B. Metrics on Positron Emission Tomography (PET) Imaging | |||||
Variable | Survival Statistics (Log-rank p-Value, adjusted p-Value) | ||||
Over All Survival (OS) | Progression free Survival (PFS) | ||||
Nodal (n = 126) | Extra Nodal (n = 94) | Nodal (n = 126) | Extra Nodal (n = 94) | ||
1 | MTV (total body) | <0.0001 * (0.0003) | <0.0001 * (0.00069) | 0.00024 * (0.00096) | 0.00017 * (0.00096) |
2 | Shape-PC1 | 0.072 (0.1152) | 0.0054 * (0.0108) | 0.052 (0.0832) | 0.0074 * (0.0148) |
3 | Size-PC | 0.16 (0.2133) | 0.0033 * (0.0088) | 0.21 (0.2400) | 0.00061 * (0.0016) |
4 | Texture-PC | 0.37 (0.4229) | 0.73 (0.730) | 0.089 (0.11897) | 0.43 (0.430) |
A. Metrics on CT Imaging: OS | |||||
Variable | Nodal (n = 126) | Extra Nodal (n = 94) | |||
HR (p-Value, q-Value) | C-Index (CI) | HR (p-Value, q-Value) | C-Index (CI) | ||
1 | MTV (total body) | 3.885 (0.00001), 0.0001 | 0.67 [0.616,0.721] | 3.644 (0.000093), 0.0004 | 0.65 [0.575, 0.721] |
2 | Shape-PC1 | 1.183 (0.5424), 0.5762 | 0.52 [0.426, 0.615] | 2.383 (0.0062), 0.0123 | 0.6 [0.516, 0.69] |
3 | Size-PC | 1.524 (0.1293), 0.172 | 0.56 [0.464, 0.647] | 2.546 (0.0021), 0.0056 | 0.62 [0.54, 0.695] |
4 | Texture-PC | 0.858 (0.5762), 0.5762 | 0.52 [0.427, 0.623] | 2.227 (0.0103), 0.0164 | 0.59 [0.495, 0.682] |
B. Metrics on PET Imaging: OS | |||||
Variable | Nodal (n = 126) | Extra Nodal (n = 94) | |||
HR (p-Value, q-value) | C-Index (CI) | HR (p-Value, q-Value) | C-Index (CI) | ||
1 | MTV (total body) | 3.885 (0.000014), 0.0001 | 0.67 [0.603, 0.733] | 3.644 (0.000093), 0.0004 | 0.65 (0.57, 0.725) |
2 | Shape-PC1 | 1.488 (0.1518), 0.2023 | 0.55 [0.438, 0.653] | 2.139 (0.0148), 0.0296 | 0.59 (0.504, 0.683) |
3 | Size-PC | 1.542 (0.1188), 0.1901 | 0.56 [0.466, 0.65] | 2.477 (0.0044), 0.0117 | 0.61 (0.516, 0.696) |
4 | Texture-PC | 1.175 (0.5568), 0.6364 | 0.52 [0.426, 0.608] | 0.899 (0.7275), 0.7275 | 0.51 (0.424, 0.602) |
C. Metrics on CT Imaging: PFS | |||||
Variable | Nodal (n = 126) | Extra Nodal (n = 94) | |||
HR (p-Value, q-Value) | C-Index (CI) | HR (p-Value, q-Value) | C-Index (CI) | ||
1 | MTV (total body) | 2.486 (0.0004), 0.0015 | 0.62 [0.553, 0.68] | 2.814 (0.0003), 0.0015 | 0.62 [0.565, 0.685] |
2 | Shape-PC1 | 1.111 (0.6683), 0.7241 | 0.51 [0.434, 0.586] | 1.921 (0.0192), 0.0309 | 0.58 [0.494, 0.662] |
3 | Size-PC | 1.355 (0.2194), 0.2926 | 0.54 [0.439, 0.639] | 2.417 (0.0014), 0.0037 | 0.61 [0.519, 0.699] |
4 | Texture-PC | 0.917 (0.7241), 0.724 | 0.52 [0.429, 0.611] | 2.323 (0.003), 0.006 | 0.6 [0.51, 0.682] |
D. Metrics on PET Imaging: PFS | |||||
Variable | Nodal (n = 126) | Extra Nodal (n = 94) | |||
HR (p-Value, q-Value) | C-Index (CI) | HR (p-Value, q-Value) | C-Index (CI) | ||
1 | MTV (total body) | 2.486 (0.0003), 0.0015 | 0.62 [0.563, 0.67] | 2.814 (0.0003), 0.0015 | 0.62 [0.56, 0.689] |
2 | Shape-PC1 | 1.615 (0.0545), 0.0872 | 0.56 [0.463, 0.659] | 2.078 (0.0087), 0.0175 | 0.6 [0.529, 0.665] |
3 | Size-PC | 1.36 (0.2135), 0.2440 | 0.54 [0.447, 0.633] | 2.598 (0.0009), 0.0025 | 0.6 [0.533, 0.674] |
4 | Texture-PC | 1.521 (0.0910), 0.1214 | 0.56 [0.477, 0.638] | 0.806 (0.4284), 0.4284 | 0.54 [0.424, 0.647] |
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Balagurunathan, Y.; Choi, J.W.; Thompson, Z.; Jain, M.; Locke, F.L. Radiomic Features Prognosticate Treatment Response in CAR-T Cell Therapy. Cancers 2025, 17, 1832. https://doi.org/10.3390/cancers17111832
Balagurunathan Y, Choi JW, Thompson Z, Jain M, Locke FL. Radiomic Features Prognosticate Treatment Response in CAR-T Cell Therapy. Cancers. 2025; 17(11):1832. https://doi.org/10.3390/cancers17111832
Chicago/Turabian StyleBalagurunathan, Yoganand, Jung W. Choi, Zachary Thompson, Michael Jain, and Frederick L. Locke. 2025. "Radiomic Features Prognosticate Treatment Response in CAR-T Cell Therapy" Cancers 17, no. 11: 1832. https://doi.org/10.3390/cancers17111832
APA StyleBalagurunathan, Y., Choi, J. W., Thompson, Z., Jain, M., & Locke, F. L. (2025). Radiomic Features Prognosticate Treatment Response in CAR-T Cell Therapy. Cancers, 17(11), 1832. https://doi.org/10.3390/cancers17111832