Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74(1):12–49.

Article  PubMed  Google Scholar 

Kumar SK, Rajkumar SV. The multiple myelomas - current concepts in cytogenetic classification and therapy. Nat Rev Clin Oncol. 2018;15(7):409–21.

Article  PubMed  CAS  Google Scholar 

Abdallah NH, Binder M, Rajkumar SV, Greipp PT, Kapoor P, Dispenzieri A, et al. A simple additive staging system for newly diagnosed multiple myeloma. Blood Cancer J. 2022;12(1):21.

Article  PubMed  PubMed Central  Google Scholar 

Chng WJ, Dispenzieri A, Chim CS, Fonseca R, Goldschmidt H, Lentzsch S, et al. IMWG consensus on risk stratification in multiple myeloma. Leukemia. 2014;28(2):269–77.

Article  PubMed  CAS  Google Scholar 

Rasche L, Chavan SS, Stephens OW, Patel PH, Tytarenko R, Ashby C, et al. Spatial genomic heterogeneity in multiple myeloma revealed by multi-region sequencing. Nat Commun. 2017;8(1):268.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Yadav U, Kumar SK, Baughn LB, Dispenzieri A, Greipp P, Ketterling R, et al. Impact of cytogenetic abnormalities on the risk of disease progression in solitary bone plasmacytomas. Blood. 2023;142(22):1871–8.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Katodritou E, Kastritis E, Gatt M, Cohen YC, Avivi I, Pouli A, et al. Real-world data on incidence, clinical characteristics and outcome of patients with macrofocal multiple myeloma (MFMM) in the era of novel therapies: a study of the Greco-Israeli collaborative myeloma working group. Am J Hematol. 2020;95(5):465–71.

Article  PubMed  CAS  Google Scholar 

Moulopoulos LA, Koutoulidis V, Hillengass J, Zamagni E, Aquerreta JD, Roche CL, et al. Recommendations for acquisition, interpretation and reporting of whole body low dose CT in patients with multiple myeloma and other plasma cell disorders: a report of the IMWG Bone Working Group. Blood Cancer J. 2018;8(10):95.

Article  PubMed  PubMed Central  Google Scholar 

Rajkumar SV, Dimopoulos MA, Palumbo A, Blade J, Merlini G, Mateos MV, et al. International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma. Lancet Oncol. 2014;15(12):e538-548.

Article  PubMed  Google Scholar 

mSMART. https://www.msmart.org/.

Rouzrokh P, Khosravi B, Faghani S, Moassefi M, Vera Garcia DV, Singh Y, et al. Mitigating bias in radiology machine learning: 1. Data handling. Radiol Artif Intell. 2022;4(5): e210290.

Article  PubMed  PubMed Central  Google Scholar 

Zhang K, Khosravi B, Vahdati S, Faghani S, Nugen F, Rassoulinejad-Mousavi SM, et al. Mitigating bias in radiology machine learning: 2. Model development. Radiol Artif Intell. 2022;4(5): e220010.

Article  PubMed  PubMed Central  Google Scholar 

Faghani S, Khosravi B, Moassefi M, Conte GM, Erickson BJ. A Comparison of three different deep learning-based models to predict the MGMT promoter methylation status in glioblastoma using brain MRI. J Digit Imaging. 2023;36(3):837–46.

Article  PubMed  PubMed Central  Google Scholar 

Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2017 21–26 July 2017; 2017. p. 2261–2269.

Moassefi M, Faghani S, Conte GM, Kowalchuk RO, Vahdati S, Crompton DJ, et al. A deep learning model for discriminating true progression from pseudoprogression in glioblastoma patients. J Neurooncol. 2022;159(2):447–55.

Article  PubMed  CAS  Google Scholar 

Singh Y, Kelm ZS, Faghani S, Erickson D, Yalon T, Bancos I, et al. Deep learning approach for differentiating indeterminate adrenal masses using CT imaging. Abdom Radiol (NY). 2023;48(10):3189–94.

Article  PubMed  Google Scholar 

Loshchilov I, Hutter F. Decoupled weight decay regularization. International Conference on Learning Representations; 2017; 2017.

Gotmare A, Keskar NS, Xiong C, Socher R. A closer look at deep learning heuristics: learning rate restarts, warmup and distillation. arXiv preprint arXiv:181013243. 2018.

Ho Y, Wookey S. The real-world-weight cross-entropy loss function: modeling the costs of mislabeling. IEEE Access. 2020;8:4806–13.

Article  Google Scholar 

Faghani S. shahriar-faghani/MM_radgen. GitHub 2024.

Faghani S, Khosravi B, Zhang K, Moassefi M, Jagtap JM, Nugen F, et al. Mitigating bias in radiology machine learning: 3. Performance metrics. Radiol Artif Intell. 2022;4(5): e220061.

Article  PubMed  PubMed Central  Google Scholar 

Binder M, Rajkumar SV, Ketterling RP, Dispenzieri A, Lacy MQ, Gertz MA, et al. Substratification of patients with newly diagnosed standard-risk multiple myeloma. Br J Haematol. 2019;185(2):254–60.

Article  PubMed  Google Scholar 

Ni B, Huang G, Huang H, Wang T, Han X, Shen L, et al. Machine learning model based on optimized radiomics feature from (18)F-FDG-PET/CT and clinical characteristics predicts prognosis of multiple myeloma: a preliminary study. J Clin Med. 2023;12(6).

Sachpekidis C, Enqvist O, Ulén J, Kopp-Schneider A, Pan L, Mai EK, et al. Artificial intelligence-based, volumetric assessment of the bone marrow metabolic activity in [(18)F]FDG PET/CT predicts survival in multiple myeloma. Eur J Nucl Med Mol Imaging. 2024.

Zhong H, Huang D, Wu J, Chen X, Chen Y, Huang C. (18)F-FDG PET/CT based radiomics features improve prediction of prognosis: multiple machine learning algorithms and multimodality applications for multiple myeloma. BMC Med Imaging. 2023;23(1):87.

Article  PubMed  PubMed Central  Google Scholar 

Liu J, Wang C, Guo W, Zeng P, Liu Y, Lang N, et al. A preliminary study using spinal MRI-based radiomics to predict high-risk cytogenetic abnormalities in multiple myeloma. Radiol Med. 2021;126(9):1226–35.

Article  PubMed  Google Scholar