Comparison of radiomics models and dual-energy material decomposition to decipher abdominal lymphoma in contrast-enhanced CT.
Artificial intelligence
DECT
Lymphoma
Oncology
Radiomics
Journal
International journal of computer assisted radiology and surgery
ISSN: 1861-6429
Titre abrégé: Int J Comput Assist Radiol Surg
Pays: Germany
ID NLM: 101499225
Informations de publication
Date de publication:
Oct 2023
Oct 2023
Historique:
received:
26
07
2022
accepted:
10
02
2023
medline:
13
9
2023
pubmed:
7
3
2023
entrez:
6
3
2023
Statut:
ppublish
Résumé
The radiologists' workload is increasing, and computational imaging techniques may have the potential to identify visually unequivocal lesions, so that the radiologist can focus on equivocal and critical cases. The purpose of this study was to assess radiomics versus dual-energy CT (DECT) material decomposition to objectively distinguish visually unequivocal abdominal lymphoma and benign lymph nodes. Retrospectively, 72 patients [m, 47; age, 63.5 (27-87) years] with nodal lymphoma (n = 27) or benign abdominal lymph nodes (n = 45) who had contrast-enhanced abdominal DECT between 06/2015 and 07/2019 were included. Three lymph nodes per patient were manually segmented to extract radiomics features and DECT material decomposition values. We used intra-class correlation analysis, Pearson correlation and LASSO to stratify a robust and non-redundant feature subset. Independent train and test data were applied on a pool of four machine learning models. Performance and permutation-based feature importance was assessed to increase the interpretability and allow for comparison of the models. Top performing models were compared by the DeLong test. About 38% (19/50) and 36% (8/22) of the train and test set patients had abdominal lymphoma. Clearer entity clusters were seen in t-SNE plots using a combination of DECT and radiomics features compared to DECT features only. Top model performances of AUC = 0.763 (CI = 0.435-0.923) were achieved for the DECT cohort and AUC = 1.000 (CI = 1.000-1.000) for the radiomics feature cohort to stratify visually unequivocal lymphomatous lymph nodes. The performance of the radiomics model was significantly (p = 0.011, DeLong) superior to the DECT model. Radiomics may have the potential to objectively stratify visually unequivocal nodal lymphoma versus benign lymph nodes. Radiomics seems superior to spectral DECT material decomposition in this use case. Therefore, artificial intelligence methodologies may not be restricted to centers with DECT equipment.
Identifiants
pubmed: 36877288
doi: 10.1007/s11548-023-02854-w
pii: 10.1007/s11548-023-02854-w
pmc: PMC10497439
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1829-1839Informations de copyright
© 2023. The Author(s).
Références
Dorfman RE, Alpern MB, Gross BH, Sandler MA (1991) Upper abdominal lymph nodes: criteria for normal size determined with CT. Radiology 180:319–322
doi: 10.1148/radiology.180.2.2068292
pubmed: 2068292
Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R et al (2009) New response evaluation criteria in solid tumors: Revised RECIST guideline (version 1.1). Eur J Cancer 45:228–247
doi: 10.1016/j.ejca.2008.10.026
pubmed: 19097774
Ollila TA, Olszewski AJ (2018) Extranodal diffuse large B cell lymphoma: molecular features, prognosis, and risk of central nervous system recurrence. Curr Treat Opt Oncol 19:38
doi: 10.1007/s11864-018-0555-8
WHO. World Health Organization. Early cancer diagnosis saves lives, cuts treatment costs [Internet] (2017). https://www.who.int/news/item/03-02-2017-early-cancer-diagnosis-saves-lives-cuts-treatment-costs
Inoue Y (2021) Radiation dose modulation of computed tomography component in positron emission tomography/computed tomography. Semin Nucl Med
Tatsugami F, Higaki T, Nakamura Y, Honda Y, Awai K (2022) Dual-energy CT: minimal essentials for radiologists. Jpn J Radiol 40(6):547–559
doi: 10.1007/s11604-021-01233-2
pubmed: 34981319
pmcid: 9162973
Liguori C, Frauenfelder G, Massaroni C, Saccomandi P, Giurazza F, Pitocco F et al (2015) Emerging clinical applications of computed tomography. Med Dev (Auckl) 8:265–278
Chandarana H, Megibow AJ, Cohen BA, Srinivasan R, Kim D, Leidecker C et al (2011) Iodine quantification with dual-energy CT: phantom study and preliminary experience with renal masses. AJR Am J Roentgenol 196:693–700
doi: 10.2214/AJR.10.5541
Millner MR, McDavid WD, Waggener RG, Dennis MJ, Payne WH, Sank VJ (1979) Extraction of information from CT scans at different energies. Med Phys 6:70–71
doi: 10.1118/1.594555
pubmed: 440238
Johnson TRC, Krauss B, Sedlmair M, Grasruck M, Bruder H, Morhard D et al (2007) Material differentiation by dual energy CT: initial experience. Eur Radiol 17:1510–1517
doi: 10.1007/s00330-006-0517-6
pubmed: 17151859
Li X, Meng X, Ye Z (2016) Iodine quantification to characterize primary lesions, metastatic and non-metastatic lymph nodes in lung cancers by dual energy computed tomography: an initial experience. Eur J Radiol 85:1219–1223
doi: 10.1016/j.ejrad.2016.03.030
pubmed: 27161073
Kaltenbach B, Wichmann JL, Pfeifer S, Albrecht MH, Booz C, Lenga L et al (2018) Iodine quantification to distinguish hepatic neuroendocrine tumor metastasis from hepatocellular carcinoma at dual-source dual-energy liver CT. Eur J Radiol 105:20–24
doi: 10.1016/j.ejrad.2018.05.019
pubmed: 30017280
Martin SS, Czwikla R, Wichmann JL, Albrecht MH, Lenga L, Savage RH et al (2018) Dual-energy CT-based iodine quantification to differentiate abdominal malignant lymphoma from lymph node metastasis. Eur J Radiol 105:255–260
doi: 10.1016/j.ejrad.2018.06.017
pubmed: 30017291
Rizzo S, Radice D, Femia M, De Marco P, Origgi D, Preda L et al (2018) Metastatic and non-metastatic lymph nodes: quantification and different distribution of iodine uptake assessed by dual-energy CT. Eur Radiol 28:760–769
doi: 10.1007/s00330-017-5015-5
pubmed: 28835993
Lennartz S, Täger P, Zopfs D, Iuga A-I, Reimer RP, Zäske C et al (2021) Lymph node assessment in prostate cancer: evaluation of iodine quantification with spectral detector CT in correlation to PSMA PET/CT. Clin Nucl Med 46:303–309
doi: 10.1097/RLU.0000000000003496
pubmed: 33443954
Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures. They Are Data Radiol 278:563–577
Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, Van Stiphout RGPM, Granton P et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446
doi: 10.1016/j.ejca.2011.11.036
pubmed: 22257792
pmcid: 4533986
Liu Y, Dou Y, Lu F, Liu L (2020) A study of radiomics parameters from dual-energy computed tomography images for lymph node metastasis evaluation in colorectal mucinous adenocarcinoma. Medicine (Baltimore) 99:e19251
doi: 10.1097/MD.0000000000019251
pubmed: 32176049
Bian Y, Guo S, Jiang H, Gao S, Shao C, Cao K et al (2022) Radiomics nomogram for the preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma. Cancer Imaging 22:4
doi: 10.1186/s40644-021-00443-1
pubmed: 34991733
pmcid: 8734356
Markotić V, Pojužina T, Radančević D, Miljko M, Pokrajčić V (2021) The Radiologist workload increase; Where is the limit? Mini review and case study. Psychiatr Danub 33:768–770
pubmed: 34718316
Cheson BD, Fisher RI, Barrington SF, Cavalli F, Schwartz LH, Zucca E et al (2014) Recommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: the Lugano classification. J Clin Oncol 32:3059–3067
doi: 10.1200/JCO.2013.54.8800
pubmed: 25113753
pmcid: 4979083
Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30:1234–1248
doi: 10.1016/j.mri.2012.06.010
pubmed: 22898692
pmcid: 3563280
Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin J-C, Pujol S et al (2012) 3D Slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 30:1323–1341
doi: 10.1016/j.mri.2012.05.001
pubmed: 22770690
pmcid: 3466397
van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107
doi: 10.1158/0008-5472.CAN-17-0339
pubmed: 29092951
pmcid: 5672828
Bernatz S, Zhdanovich Y, Ackermann J, Koch I, Wild PJ, Pinto D et al (2021) Impact of rescanning and repositioning on radiomic features employing a multi-object phantom in magnetic resonance imaging. Sci Rep 11:1–13
doi: 10.1038/s41598-021-93756-x
Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762
doi: 10.1038/nrclinonc.2017.141
pubmed: 28975929
Kluyver T, Ragan-Kelley B, Pérez F, Granger B, Bussonnier M, Frederic J et al (2016) Jupyter notebooks—a publishing format for reproducible computational workflows. In: Positioning and power in academic publishing: players, agents and agendas—proceedings of the 20th international conference on electronic publishing, ELPUB 2016, pp 87–90
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830
Vallat R (2018) Pingouin: statistics in python. J Open Source Softw 3:1026
doi: 10.21105/joss.01026
Starmans MPA, Voort SR, Van Der PT, Timbergen MJM, Vos M, Guillaume A et al. (2021) Reproducible radiomics through automated machine learning validated on twelve clinical applications
Sun X, Xu W (2014) Fast implementation of DeLong’s algorithm for comparing the areas under. Correlat Receiv 21:1389–1393
Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A et al (2019) Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin 69:caac21552
doi: 10.3322/caac.21552
De Cecco CN, Darnell A, Rengo M, Muscogiuri G, Bellini D, Ayuso C et al (2012) Dual-energy CT: oncologic applications. AJR Am J Roentgenol 199:98–105
doi: 10.2214/AJR.12.9207
Feng Q, Hu Q, Liu Y, Yang T, Yin Z (2020) Diagnosis of triple negative breast cancer based on radiomics signatures extracted from preoperative contrast-enhanced chest computed tomography. BMC Cancer 20:579
doi: 10.1186/s12885-020-07053-3
pubmed: 32571245
pmcid: 7309976
Bedrikovetski S, Dudi-Venkata NN, Kroon HM, Seow W, Vather R, Carneiro G et al (2021) Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis. BMC Cancer 21:1058
doi: 10.1186/s12885-021-08773-w
pubmed: 34565338
pmcid: 8474828
Yang J, Wang L, Qin J, Du J, Ding M, Niu T et al (2022) Multi-view learning for lymph node metastasis prediction using tumor and nodal radiomics in gastric cancer. Phys Med Biol 67:5
doi: 10.1088/1361-6560/ac515b
Xie Y, Zhao H, Guo Y, Meng F, Liu X, Zhang Y et al (2021) A PET/CT nomogram incorporating SUVmax and CT radiomics for preoperative nodal staging in non-small cell lung cancer. Eur Radiol 31:6030–6038
doi: 10.1007/s00330-020-07624-9
pubmed: 33560457
pmcid: 8270849
Chauvie S, Ceriani L, Zucca E (2021) Radiomics in malignant lymphomas. Lymphoma
Enke JS, Moltz JH, D’Anastasi M, Kunz WG, Schmidt C, Maurus S et al (2022) Radiomics features of the spleen as surrogates for CT-based lymphoma diagnosis and subtype differentiation. Cancers (Basel) 14:713
doi: 10.3390/cancers14030713
pubmed: 35158980
Lisson CS, Lisson CG, Achilles S, Mezger MF, Wolf D, Schmidt SA et al (2022) Longitudinal CT imaging to explore the predictive power of 3D radiomic tumour heterogeneity in precise imaging of mantle cell lymphoma (MCL). Cancers (Basel) 14:393
doi: 10.3390/cancers14020393
pubmed: 35053554
Dong C, Zheng Y-M, Li J, Wu Z-J, Yang Z-T, Li X-L et al (2022) A CT-based radiomics nomogram for differentiation of squamous cell carcinoma and non-Hodgkin’s lymphoma of the palatine tonsil. Eur Radiol 32:243–253
doi: 10.1007/s00330-021-08153-9
pubmed: 34236464