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The added diagnostic value of 18 F-FDG PET/CT radiomic analysis in multiple myeloma patients with negative visual
Shuxian An1, Gan Huang, Xiaofeng Yu
1Department of Nuclear Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Purpose:
A small number of patients diagnosed with multiple myeloma (MM) by bone marrow aspiration reported as being disease-free on 18 F-FDG PET/CT. We aim to evaluate the diagnostic value of radiomics approach in patients with MM who were negative by visual analysis.
Materials And Methods:
Thirty-three patients judged negative by visual analysis were assigned to the MM group. Contemporaneous 31 disease-free patients served as the control group. 70% of the whole data set was used as training set (23 from MM group and 22 from control group) and 30% as testing set (10 from MM group and 9 from control group). Axial skeleton volumes were automatically segmented and high-dimensional imaging features were extracted from PET and CT. The unsupervised machine learning method was used to filter and reduce the dimensions of the extracted features. Random forest was used to construct the prediction model and then validated with 10-fold cross-validation and evaluated on the independent testing set.
Results:
One thousand seven hundred two quantitative features were extracted from PET and CT. Of those, three first-order and one high-order imaging features were uncorrelated. With the cross-validation on the training group, the sensitivity, specificity, accuracy and area under the curve of random forest were 0.850, 0.792, 0.818 and 0.894, respectively. On the independent testing set, the accuracy of the model was 0.850 and the area under the curve was 0.909.
Conclusion:
Radiomic analysis based on 18 F-FDG PET/CT using machine learning model provides a quantitative, objective and efficient mechanism for diagnosing patients with MM who were negative by visual analysis.
Insights
Radiomics analysis of 18 F-FDG PET/CT scans can accurately diagnose multiple myeloma (MM) in patients who appear disease-free on visual analysis. This machine learning approach offers an objective method for MM diagnosis.
Area of Science:
- Oncology
- Radiology
- Medical Imaging
Background:
- Multiple myeloma (MM) diagnosis can be challenging, with some patients appearing disease-free on standard 18 F-FDG PET/CT visual assessment.
- This diagnostic challenge necessitates advanced analytical techniques to improve detection rates.
Purpose of the Study:
- To evaluate the diagnostic performance of a radiomics approach in identifying multiple myeloma (MM) in patients who were visually negative on 18 F-FDG PET/CT.
- To assess the utility of machine learning models in conjunction with radiomics for MM detection.
Main Methods:
- A radiomics approach was applied to 18 F-FDG PET/CT data from 33 MM patients (visually negative) and 31 controls.
- High-dimensional imaging features were extracted, reduced using unsupervised machine learning, and a random forest prediction model was constructed.
- The model was validated using 10-fold cross-validation and tested on an independent dataset.
Main Results:
- Over 1700 quantitative imaging features were extracted from PET and CT scans.
- The random forest model achieved a sensitivity of 0.850, specificity of 0.792, and accuracy of 0.818 on the training set.
- On the independent testing set, the model demonstrated an accuracy of 0.850 and an area under the curve of 0.909.
Conclusions:
- Radiomic analysis of 18 F-FDG PET/CT, powered by machine learning, offers a quantitative and objective method for diagnosing MM in visually negative patients.
- This approach enhances diagnostic efficiency and accuracy in challenging MM cases.
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