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Radiomics based on biparametric MRI for the detection of significant residual prostate cancer after androgen deprivation therapy: using whole-mount histopathology as reference standard


1 Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
2 Department of Oncology, Shanghai Medical College of Fudan University, Shanghai 200032, China
3 Department of Urology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
4 Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
5 MR Research, GE Healthcare, Shanghai 200032, China

Correspondence Address:
Liang-Ping Zhou,
Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College of Fudan University, Shanghai 200032
China
Xiao-Hang Liu,
Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College of Fudan University, Shanghai 200032
China
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/aja202215

We aimed to study radiomics approach based on biparametric magnetic resonance imaging (MRI) for determining significant residual cancer after androgen deprivation therapy (ADT). Ninety-two post-ADT prostate cancer patients underwent MRI before prostatectomy (62 with significant residual disease and 30 with complete response or minimum residual disease [CR/MRD]). Totally, 100 significant residual, 52 CR/MRD lesions, and 70 benign tissues were selected according to pathology. First, 381 radiomics features were extracted from T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient (ADC) maps. Optimal features were selected using a support vector machine with a recursive feature elimination algorithm (SVM-RFE). Then, ADC values of significant residual, CR/MRD lesions, and benign tissues were compared by one-way analysis of variance. Logistic regression was used to construct models with SVM features to differentiate between each pair of tissues. Third, the efficiencies of ADC value and radiomics models for differentiating the three tissues were assessed by area under receiver operating characteristic curve (AUC). The ADC value (mean ± standard deviation [s.d.]) of significant residual lesions ([1.10 ± 0.02] × 10-3 mm2 s-1) was significantly lower than that of CR/MRD ([1.17 ± 0.02] × 10-3 mm2 s-1), which was significantly lower than that of benign tissues ([1.30 ± 0.02] × 10-3 mm2 s-1; both P < 0.05). The SVM feature models were comparable to ADC value in distinguishing CR/MRD from benign tissue (AUC: 0.766 vs 0.792) and distinguishing residual from benign tissue (AUC: 0.825 vs 0.835) (both P > 0.05), but superior to ADC value in differentiating significant residual from CR/MRD (AUC: 0.748 vs 0.558; P = 0.041). Radiomics approach with biparametric MRI could promote the detection of significant residual prostate cancer after ADT.


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    -  Chen ZZ
    -  Gu WJ
    -  Zhou BN
    -  Liu W
    -  Gan HL
    -  Zhang Y
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