Bone marrow segmentation and radiomics analysis of [18F]FDG PET/CT images for measurable residual disease assessment in multiple myeloma

Milara, Eva; Gómez‐Grande, Adolfo; Tomás-Soler, Sebastián; Seiffert, Alexander P.; Alonso, Rafael; Gómez, Enrique J.; Martínez‐López, Joaquín; Sánchez-González, Patricia

doi:10.1016/j.cmpb.2022.107083

Cited by 12 publications

(23 citation statements)

References 30 publications

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“…Radiomics features may quantify structural characteristics of bone marrow changes in MRI images and may be implemented as a complementary prognosis evaluation tool [ 25 ]. Some studies have shown MRI-based or PET/CT-based radiomics features may provide valuable information for image-based assessment of MRD and prediction of the therapy response [ 16 , 17 , 18 , 26 ]. Jamet B [ 27 ] tried to evaluate the potential prognostic value of textural features extracted from FDG-PET/CT in MM framework in addition to conventional PET-derived metabolic features and usual clinical/biological/genetic parameters.…”

Section: Discussionmentioning

confidence: 99%

“…It also showed the value in disease follow-up, treatment options, and prognosis prediction. Bone marrow radiomics features extracted from 18 F-FDG PET/CT may provide some information of MRD [ 16 ]. In a small sample size study, radiomics models based on MRI could also predict the response to bortezomib-based therapy in MM patients [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Model Based on Optimized Radiomics Feature from 18F-FDG-PET/CT and Clinical Characteristics Predicts Prognosis of Multiple Myeloma: A Preliminary Study

Huang

et al. 2023

JCM

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Objects: To evaluate the prognostic value of radiomics features extracted from 18F-FDG-PET/CT images and integrated with clinical characteristics and conventional PET/CT metrics in newly diagnosed multiple myeloma (NDMM) patients. Methods: We retrospectively reviewed baseline clinical information and 18F-FDG-PET/CT imaging data of MM patients with 18F-FDG-PET/CT. Multivariate Cox regression models involving different combinations were constructed, and stepwise regression was performed: (1) radiomics features of PET/CT alone (Rad Model); (2) Using clinical data (including clinical/laboratory parameters and conventional PET/CT metrics) only (Cli Model); (3) Combination radiomics features and clinical data (Cli-Rad Model). Model performance was evaluated by C-index and Net Reclassification Index (NRI). Results: Ninety-eight patients with NDMM who underwent 18F-FDG-PET/CT between 2014 and 2019 were included in this study. Combining radiomics features from PET/CT with clinical data showed higher prognostic performance than models with radiomics features or clinical data alone (C-index 0.790 vs. 0.675 vs. 0.736 in training cohort; 0.698 vs. 0.651 vs. 0.563 in validation cohort; AUC 0.761, sensitivity 56.7%, specificity 85.7%, p < 0.05 in training cohort and AUC 0.650, sensitivity 80.0%, specificity78.6%, p < 0.05 in validation cohort) When clinical data was combined with radiomics, an increase in the performance of the model was observed (NRI > 0). Conclusions: Radiomics features extracted from the PET and CT components of baseline 18F-FDG-PET/CT images may become an effective complement to provide prognostic information; therefore, radiomics features combined with clinical characteristic may provide clinical value for MM prognosis prediction.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Model Based on Optimized Radiomics Feature from 18F-FDG-PET/CT and Clinical Characteristics Predicts Prognosis of Multiple Myeloma: A Preliminary Study

Huang

et al. 2023

JCM

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“…The image preprocessing methodology and bone marrow segmentation is based on Milara et al [ 24 ]. This segmentation is based on the application of different thresholding and morphological operations on the CT image to obtain de skeleton mask from the humeri, femora and torso regions.…”

Section: Methodsmentioning

confidence: 99%

“…Indeed, for patients newly diagnosed MM, radiomics quantification of [ 18 F]FDG PET images have been studied as prognostic indicators of worse survivall [ 21 , 22 ]. Moreover, machine learning (ML) models based on radiomic features for MM diagnosis [ 23 ] and MRD detection [ 24 ] with [ 18 F]FDG PET images has been previously studied.…”

Section: Introductionmentioning

confidence: 99%

“…In the study of Milara et al [ 24 ], a segmentation methodology along with the analysis of radiomic features extracted from [ 18 F]FDG PET/CT images in MM patients was proposed and implemented in a software tool for supporting visual assessment of MRD. The proposed segmentation evaluated the whole bone marrow, hindering the relationship between MFC results, taken from small and specific biopsy sites such as the iliac crest and sternum, and [ 18 F]FDG PET visual assessment.…”

Section: Introductionmentioning

confidence: 99%

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Radiomics analysis of bone marrow biopsy locations in [18F]FDG PET/CT images for measurable residual disease assessment in multiple myeloma

Milara,

Alonso,

Masseing

et al. 2023

Phys Eng Sci Med

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The combination of visual assessment of whole body [18F]FDG PET images and evaluation of bone marrow samples by Multiparameter Flow Cytometry (MFC) or Next-Generation Sequencing (NGS) is currently the most common clinical practice for the detection of Measurable Residual Disease (MRD) in Multiple Myeloma (MM) patients. In this study, radiomic features extracted from the bone marrow biopsy locations are analyzed and compared to those extracted from the whole bone marrow in order to study the representativeness of these biopsy locations in the image-based MRD assessment. Whole body [18F]FDG PET of 39 patients with newly diagnosed MM were included in the database, and visually evaluated by experts in nuclear medicine. A methodology for the segmentation of biopsy sites from PET images, including sternum and posterior iliac crest, and their subsequent quantification is proposed. First, starting from the bone marrow segmentation, a segmentation of the biopsy sites is performed. Then, segmentations are quantified extracting SUV metrics and radiomic features from the [18F]FDG PET images and are evaluated by Mann–Whitney U-tests as valuable features differentiating PET+/PET− and MFC+ /MFC− groups. Moreover, correlation between whole bone marrow and biopsy sites is studied by Spearman ρ rank. Classification performance of the radiomics features is evaluated applying seven machine learning algorithms. Statistical analyses reveal that some images features are significant in PET+/PET− differentiation, such as SUVmax, Gray Level Non-Uniformity or Entropy, especially with a balanced database where 16 of the features show a p value < 0.001. Correlation analyses between whole bone marrow and biopsy sites results in significant and acceptable coefficients, with 11 of the variables reaching a correlation coefficient greater than 0.7, with a maximum of 0.853. Machine learning algorithms demonstrate high performances in PET+/PET− classification reaching a maximum AUC of 0.974, but not for MFC+/MFC− classification. The results demonstrate the representativeness of sample sites as well as the effectiveness of extracted features (SUV metrics and radiomic features) from the [18F]FDG PET images in MRD assessment in MM patients.

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Automatic Skeleton Segmentation in CT Images Based on U-Net

Milara,

Gómez-Grande,

Sarandeses

et al. 2024

J Digit Imaging. Inform. med.

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Bone metastasis, emerging oncological therapies, and osteoporosis represent some of the distinct clinical contexts which can result in morphological alterations in bone structure. The visual assessment of these changes through anatomical images is considered suboptimal, emphasizing the importance of precise skeletal segmentation as a valuable aid for its evaluation. In the present study, a neural network model for automatic skeleton segmentation from bidimensional computerized tomography (CT) slices is proposed. A total of 77 CT images and their semimanual skeleton segmentation from two acquisition protocols (whole-body and femur-to-head) are used to form a training group and a testing group. Preprocessing of the images includes four main steps: stretcher removal, thresholding, image clipping, and normalization (with two different techniques: interpatient and intrapatient). Subsequently, five different sets are created and arranged in a randomized order for the training phase. A neural network model based on U-Net architecture is implemented with different values of the number of channels in each feature map and number of epochs. The model with the best performance obtains a Jaccard index (IoU) of 0.959 and a Dice index of 0.979. The resultant model demonstrates the potential of deep learning applied in medical images and proving its utility in bone segmentation.

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Bone marrow segmentation and radiomics analysis of [18F]FDG PET/CT images for measurable residual disease assessment in multiple myeloma

Cited by 12 publications

References 30 publications

Machine Learning Model Based on Optimized Radiomics Feature from 18F-FDG-PET/CT and Clinical Characteristics Predicts Prognosis of Multiple Myeloma: A Preliminary Study

Machine Learning Model Based on Optimized Radiomics Feature from 18F-FDG-PET/CT and Clinical Characteristics Predicts Prognosis of Multiple Myeloma: A Preliminary Study

Radiomics analysis of bone marrow biopsy locations in [18F]FDG PET/CT images for measurable residual disease assessment in multiple myeloma

Automatic Skeleton Segmentation in CT Images Based on U-Net

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