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Deep Semisupervised Transfer Learning for Fully Automated Whole-Body Tumor Quantification and Prognosis of Cancer on PET/CT. | LitMetric

AI Article Synopsis

  • Automatic detection and characterization of cancer using a deep, semisupervised transfer learning approach was developed for tumor segmentation and prognosis from PET/CT scans, focusing on various cancer types.
  • The study analyzed 611 F-FDG and 408 PSMA PET/CT scans to evaluate the segmentation performance through metrics like true-positive rate and Dice similarity coefficient, yielding strong results across different cancer types.
  • Prognostic models created from the segmented images showed significant accuracy in risk stratification for prostate cancer and overall survival predictions for head and neck cancer, along with pathologic response predictions for breast cancer after chemotherapy.

Article Abstract

Automatic detection and characterization of cancer are important clinical needs to optimize early treatment. We developed a deep, semisupervised transfer learning approach for fully automated, whole-body tumor segmentation and prognosis on PET/CT. This retrospective study consisted of 611 F-FDG PET/CT scans of patients with lung cancer, melanoma, lymphoma, head and neck cancer, and breast cancer and 408 prostate-specific membrane antigen (PSMA) PET/CT scans of patients with prostate cancer. The approach had a nnU-net backbone and learned the segmentation task on F-FDG and PSMA PET/CT images using limited annotations and radiomics analysis. True-positive rate and Dice similarity coefficient were assessed to evaluate segmentation performance. Prognostic models were developed using imaging measures extracted from predicted segmentations to perform risk stratification of prostate cancer based on follow-up prostate-specific antigen levels, survival estimation of head and neck cancer by the Kaplan-Meier method and Cox regression analysis, and pathologic complete response prediction of breast cancer after neoadjuvant chemotherapy. Overall accuracy and area under the receiver-operating-characteristic (AUC) curve were assessed. Our approach yielded median true-positive rates of 0.75, 0.85, 0.87, and 0.75 and median Dice similarity coefficients of 0.81, 0.76, 0.83, and 0.73 for patients with lung cancer, melanoma, lymphoma, and prostate cancer, respectively, on the tumor segmentation task. The risk model for prostate cancer yielded an overall accuracy of 0.83 and an AUC of 0.86. Patients classified as low- to intermediate- and high-risk had mean follow-up prostate-specific antigen levels of 18.61 and 727.46 ng/mL, respectively ( < 0.05). The risk score for head and neck cancer was significantly associated with overall survival by univariable and multivariable Cox regression analyses ( < 0.05). Predictive models for breast cancer predicted pathologic complete response using only pretherapy imaging measures and both pre- and posttherapy measures with accuracies of 0.72 and 0.84 and AUCs of 0.72 and 0.76, respectively. The proposed approach demonstrated accurate tumor segmentation and prognosis in patients across 6 cancer types on F-FDG and PSMA PET/CT scans.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10995523PMC
http://dx.doi.org/10.2967/jnumed.123.267048DOI Listing

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