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Automated Whole-Body Tumor Segmentation and Prognosis of Cancer on PET/CT
DescriptionBackground: Cancer is the second leading cause of death in the United States [1]. Automatic characterization of malignant disease is an important clinical need to facilitate early detection and treatment of cancer [2]. Advances in machine learning (ML) and deep learning (DL) have shown significant promise for radiological and oncological applications [3]. Radiomic analysis extracts quantitative features from radiologic data about a cancerous tumor [4]. DL methods require large training datasets with sufficiently annotated images, which are difficult to obtain for radiological applications. The objective of this study was to develop a deep semi-supervised transfer learning approach for automated whole-body tumor segmentation and prognosis on positron emission tomography (PET)/computed tomography (CT) scans using limited annotations (Fig. 1a).
Methods: Five datasets consisting of 1,019 prostate, lung, melanoma, lymphoma, head and neck, and breast cancer patients with prostate-specific membrane antigen (PSMA) and fluorodeoxyglucose (FDG) PET/CT scans were used in this study (Table 1). A nnUnet backbone was cross-validated on the tumor segmentation task via a 5-fold cross-validation. Predicted segmentations were iteratively improved using radiomic analysis. Transfer learning generalized the segmentation task across PSMA and FDG PET/CT. Segmentation accuracy was evaluated on true positive rate (TPR), positive predictive value (PPV), Dice similarity coefficient (DSC), false discovery rate (FDR), true negative rate (TNR), and negative predictive value (NPV). Imaging measures quantifying molecular tumor burden and uptake were extracted from the predicted segmentations. A risk stratification model was developed for prostate cancer by combining the extracted imaging measures and was evaluated on follow-up prostate-specific antigen (PSA) levels. A risk stratification model was developed for head and neck cancer patients by combining imaging measures and American Joint Committee on Cancer (AJCC) staging and was evaluated via Kaplan-Meier survival analysis. A prognostic model was developed to predict pathological response of breast cancer patients to neoadjuvant chemotherapy using imaging measures from pre-therapy and post-therapy PET/CT scans. Prognostic models were evaluated on overall accuracy and area under the receiver operating characteristic (AUROC) curve. Statistically significant differences were inferred using a Wilcoxon rank-sum test.
Results: Accuracy metrics and illustrative examples of predicted tumor segmentations are shown in Table 2 and Fig. 1b. The risk stratification model yielded an overall accuracy of 0.83 and an AUROC of 0.86 in stratifying prostate cancer patients (Fig. 1c). Median follow-up PSA levels in the low-intermediate and high risk groups were 1.19 ng/mL and 53.20 ng/mL (P < 0.05). Head and neck cancer patients were stratified into low, intermediate, and high risk groups with significantly different Kaplan-Meier survival curves by the log-rank test (Fig. 1d). A prognostic model using imaging measures from pre-therapy scans predicted pathological complete response (pCR) in breast cancer patients with an accuracy of 0.72 and an AUROC of 0.72. The model using imaging measures from both pre-therapy and post-therapy scans predicted pCR in breast cancer patients with an accuracy of 0.84 and an AUROC of 0.76.
Conclusion: A deep semi-supervised transfer learning approach was developed and demonstrated accurate tumor segmentation, quantification, and prognosis on PET/CT of patients across six cancer types.
Methods: Five datasets consisting of 1,019 prostate, lung, melanoma, lymphoma, head and neck, and breast cancer patients with prostate-specific membrane antigen (PSMA) and fluorodeoxyglucose (FDG) PET/CT scans were used in this study (Table 1). A nnUnet backbone was cross-validated on the tumor segmentation task via a 5-fold cross-validation. Predicted segmentations were iteratively improved using radiomic analysis. Transfer learning generalized the segmentation task across PSMA and FDG PET/CT. Segmentation accuracy was evaluated on true positive rate (TPR), positive predictive value (PPV), Dice similarity coefficient (DSC), false discovery rate (FDR), true negative rate (TNR), and negative predictive value (NPV). Imaging measures quantifying molecular tumor burden and uptake were extracted from the predicted segmentations. A risk stratification model was developed for prostate cancer by combining the extracted imaging measures and was evaluated on follow-up prostate-specific antigen (PSA) levels. A risk stratification model was developed for head and neck cancer patients by combining imaging measures and American Joint Committee on Cancer (AJCC) staging and was evaluated via Kaplan-Meier survival analysis. A prognostic model was developed to predict pathological response of breast cancer patients to neoadjuvant chemotherapy using imaging measures from pre-therapy and post-therapy PET/CT scans. Prognostic models were evaluated on overall accuracy and area under the receiver operating characteristic (AUROC) curve. Statistically significant differences were inferred using a Wilcoxon rank-sum test.
Results: Accuracy metrics and illustrative examples of predicted tumor segmentations are shown in Table 2 and Fig. 1b. The risk stratification model yielded an overall accuracy of 0.83 and an AUROC of 0.86 in stratifying prostate cancer patients (Fig. 1c). Median follow-up PSA levels in the low-intermediate and high risk groups were 1.19 ng/mL and 53.20 ng/mL (P < 0.05). Head and neck cancer patients were stratified into low, intermediate, and high risk groups with significantly different Kaplan-Meier survival curves by the log-rank test (Fig. 1d). A prognostic model using imaging measures from pre-therapy scans predicted pathological complete response (pCR) in breast cancer patients with an accuracy of 0.72 and an AUROC of 0.72. The model using imaging measures from both pre-therapy and post-therapy scans predicted pCR in breast cancer patients with an accuracy of 0.84 and an AUROC of 0.76.
Conclusion: A deep semi-supervised transfer learning approach was developed and demonstrated accurate tumor segmentation, quantification, and prognosis on PET/CT of patients across six cancer types.
Event Type
Workshop
TimeSunday, 12 November 20234pm - 4:15pm MST
Location506
Applications
State of the Practice
W