CT-based radiomics model with machine learning for predicting primary treatment failure in diffuse large B-cell Lymphoma

Santiago, Raoul; Jiménez, Johanna Ortiz; Forghani, Reza; Muthukrishnan, N. Moorthy; Corpo, Olivier Del; Karthigesu, Shairabi; Haider, Muhammad Yahya; Reinhold, Caroline; Assouline, Sarit

doi:10.1016/j.tranon.2021.101188

Cited by 12 publications

(10 citation statements)

References 30 publications

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“…Nine studies applied ML to prognostication or predicting responses to therapy in patients with hematological malignancies ( Table 3 ): (i) predicting outcomes (overall survival or progression-free survival) in patients with extranodal NK/T-cell lymphoma, nasal type ( 69 ), multiple myeloma ( 70 , 75 ), DLBCL ( 71 ), and mantle cell lymphoma ( 73 ) or (ii) predicting responses to therapy in patients with DLBCL ( 68 , 76 ) and HL ( 74 ). One study aimed to identify high-risk cytogenetic (HRC) multiple myeloma patients by applying ML to MRI images ( 72 ).…”

Section: Resultsmentioning

confidence: 99%

“…Several different ML approaches were applied including a logistic regression classifier ( 68 , 72 ), random survival forests (RSFs ( 70 );), weakly supervised deep learning ( 69 ), ANN/CNNs ( 71 , 73 ), SVM ( 72 , 74 ), RF ( 72 , 75 , 76 ), DT ( 72 ), K-NN ( 72 ), and XGBoost ( 72 ). The models used a range of features including not only automatically extracted radiomic features but also clinicopathological variables in two studies ( 70 , 75 ), laboratory variables in one study ( 73 ), and two additional radiological features (nodal site and subjective necrosis) in one study ( 76 ). The models were validated by splitting the data or cross-validation, and no study tested the models on independent validation sets.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

The application of machine learning to imaging in hematological oncology: A scoping review

Kotsyfakis¹,

Iliaki-Giannakoudaki²,

Anagnostopoulos³

et al. 2022

Front. Oncol.

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BackgroundHere, we conducted a scoping review to (i) establish which machine learning (ML) methods have been applied to hematological malignancy imaging; (ii) establish how ML is being applied to hematological cancer radiology; and (iii) identify addressable research gaps.MethodsThe review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews guidelines. The inclusion criteria were (i) pediatric and adult patients with suspected or confirmed hematological malignancy undergoing imaging (population); (ii) any study using ML techniques to derive models using radiological images to apply to the clinical management of these patients (concept); and (iii) original research articles conducted in any setting globally (context). Quality Assessment of Diagnostic Accuracy Studies 2 criteria were used to assess diagnostic and segmentation studies, while the Newcastle–Ottawa scale was used to assess the quality of observational studies.ResultsOf 53 eligible studies, 33 applied diverse ML techniques to diagnose hematological malignancies or to differentiate them from other diseases, especially discriminating gliomas from primary central nervous system lymphomas (n=18); 11 applied ML to segmentation tasks, while 9 applied ML to prognostication or predicting therapeutic responses, especially for diffuse large B-cell lymphoma. All studies reported discrimination statistics, but no study calculated calibration statistics. Every diagnostic/segmentation study had a high risk of bias due to their case–control design; many studies failed to provide adequate details of the reference standard; and only a few studies used independent validation.ConclusionTo deliver validated ML-based models to radiologists managing hematological malignancies, future studies should (i) adhere to standardized, high-quality reporting guidelines such as the Checklist for Artificial Intelligence in Medical Imaging; (ii) validate models in independent cohorts; (ii) standardize volume segmentation methods for segmentation tasks; (iv) establish comprehensive prospective studies that include different tumor grades, comparisons with radiologists, optimal imaging modalities, sequences, and planes; (v) include side-by-side comparisons of different methods; and (vi) include low- and middle-income countries in multicentric studies to enhance generalizability and reduce inequity.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

The application of machine learning to imaging in hematological oncology: A scoping review

Kotsyfakis¹,

Iliaki-Giannakoudaki²,

Anagnostopoulos³

et al. 2022

Front. Oncol.

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“…One report of nodal texture analysis in pediatric patients found a sensitivity in the range of 82.4–88.8% and a specificity in the range of 72.4–86% for detecting malignant lymph nodes 48 . Although there have been studies using radiomics or texture analysis for evaluation of neoplastic cervical lymph nodes in adults 33 , 47 , 49 , 51 , 53 , 58 , 59 , to our knowledge, our study is the first to assess radiomic features of NTM lymphadenitis. In addition, our analysis includes comparisons with pyogenic lymphadenitis, reactive and proliferative lymphadenopathy with a much larger sample size.…”

Section: Discussionmentioning

confidence: 99%

“…Recently more publications discussing the utility of radiomics and machine learning for the evaluation of lymph nodes have demonstrated the utility of such approaches 33 , 45 – 57 . Lymph node radiomic features have been suggested to be highly predictive of malignant versus benign etiology 33 , 45 , 46 , 48 , 50 , 54 , 58 . One study carried out in the pediatric population reported a sensitivity of up to 82.4% and a specificity of 86.2% 48 .…”

Section: Introductionmentioning

confidence: 99%

Radiomics and machine learning for the diagnosis of pediatric cervical non-tuberculous mycobacterial lymphadenitis

Bulushi

Saint‐Martin

Muthukrishnan

et al. 2022

Sci Rep

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Non-tuberculous mycobacterial (NTM) infection is an emerging infectious entity that often presents as lymphadenitis in the pediatric age group. Current practice involves invasive testing and excisional biopsy to diagnose NTM lymphadenitis. In this study, we performed a retrospective analysis of 249 lymph nodes selected from 143 CT scans of pediatric patients presenting with lymphadenopathy at the Montreal Children’s Hospital between 2005 and 2018. A Random Forest classifier was trained on the ten most discriminative features from a set of 1231 radiomic features. The model classifying nodes as pyogenic, NTM, reactive, or proliferative lymphadenopathy achieved an accuracy of 72%, a precision of 68%, and a recall of 70%. Between NTM and all other causes of lymphadenopathy, the model achieved an area under the curve (AUC) of 89%. Between NTM and pyogenic lymphadenitis, the model achieved an AUC of 90%. Between NTM and the reactive and proliferative lymphadenopathy groups, the model achieved an AUC of 93%. These results indicate that radiomics can achieve a high accuracy for classification of NTM lymphadenitis. Such a non-invasive highly accurate diagnostic approach has the potential to reduce the need for invasive procedures in the pediatric population.

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“…Similarly, Schöder et al [11] utilized 18 FDG-PET scans at baseline, interim, and end of treatment (EoT) to identify biomarkers of response that are predictive of remission and survival. Recently, Santiago et al [12] built a CT-based radiomics approach that utilizes random forest (RF) machine learning for predicting refractory DLBCL. Senjo et al [13] measured metabolic heterogeneity using 18 FDG-PET/CT to predict a worse prognosis.…”

Section: Introductionmentioning

confidence: 99%

Multimodal deep learning model on interim [18F]FDG PET/CT for predicting primary treatment failure in diffuse large B-cell lymphoma

Cheng

Shi²,

Huang³

et al. 2022

Eur Radiol

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Purpose: Prediction of primary treatment failure (PTF) is necessary for patients suffering from diffuse large B-cell lymphoma (DLBCL), since it serves as a prominent means for enhancing front-line outcomes. Utilizing interim 18 F-Fluorodeoxyglucose (FDG) positron emission tomography and computed tomography (PET/CT) image data, we aimed to construct multimodal deep learning (MDL) models to predict possible PTF of low-risk DLBCL, which could enable individualized treatment decision-making in clinical practice.Methods: From June 2016 to November 2020, 205 DLBCL patients undergoing interim 18 F-FDG PET-CT scans and the front-line standard-of-care were enrolled. We also collected other 44 patients for the external validation. We built a powerful backbone by redesigning the famous visual recognition network named Conv-LSTM in aspects of network architecture and learning strategy. On top of our improved backbone, multiple MDL models using different feature fusion strategies were developed and compared, including pixel intermixing model, separate channel model, separate branch model, quantitative weighting model, and hybrid learning model. Moreover, we proposed to use a contrastive training objective in the above best model to enhance the modal correlation of semantic embeddings for further improving prediction performance. For visualization, the region of interest was instructed using an activation map.Results: The MDL model using the hybrid learning strategy provided the best performance in predicting possible PTF with the accuracy of 89.76% (95% con dence interval [CI]: 84.85%-93.20%) in the test cohort. After further optimized by contrastive objective training, the accuracy was improved to 91.22% (95% CI: 86.55%-94.37%). The AUCs of contrastive hybrid learning achieved 0.926 and 0.925 in the test cohort and external validation cohort, respectively. Conclusion: Our model showed outstanding performance for predicting PTF of low-risk DLBCL and hold promise of improving clinical individualized treatment strategies.

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CT-based radiomics model with machine learning for predicting primary treatment failure in diffuse large B-cell Lymphoma

Abstract: Highlights CT-based radiomics with machine learning classifier is able to accurately predict primary refractory Diffuse Large B Cell Lymphomas (DLBCL). The radiomics model exhibits a better discrimination for refractory DLBCL identification compared to available standard clinical criteria.

Cited by 12 publications

References 30 publications

The application of machine learning to imaging in hematological oncology: A scoping review

The application of machine learning to imaging in hematological oncology: A scoping review

Radiomics and machine learning for the diagnosis of pediatric cervical non-tuberculous mycobacterial lymphadenitis

Multimodal deep learning model on interim [18F]FDG PET/CT for predicting primary treatment failure in diffuse large B-cell lymphoma

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