To pre-operatively and non-invasively predict 1p/19q co-deletion in grade II and III (lower-grade) glioma based on a radiomics method using magnetic resonance imaging (MRI). We obtained 105 patients pathologically diagnosed with lower-grade glioma. We extracted 647 MRI-based features from T2-weighted images and selected discriminative features by lasso logistic regression approaches on the training cohort (n=69). Radiomics, clinical, and combined models were constructed separately to verify the predictive performance of the radiomics signature. The predictability of the three models were validated on a time-independent validation cohort (n = 36). Finally, 7 discriminative radiomic features were used constructed radiomics signature, which demonstrated satisfied performance on both the training and validation cohorts with AUCs of 0.822 and 0.731, respectively. Particularly, the combined model incorporating the radiomics signature and the clinic-radiological factors achieved the best discriminative capability with AUCs of 0.911 and 0.866 for training and validation cohorts, respectively.
In hepatocellular carcinoma (HCC), more than one third of patients were accompanied by macrovascular invasion (MaVI) during diagnosis and treatment. HCCs with MaVI presented with aggressive tumor behavior and poor survival. Early identification of HCCs at high risk of MaVI would promote adequate preoperative treatment strategy making, so as to prolong the patient survival. Thus, we aimed to develop a computed tomography (CT)-based radiomics model to preoperatively predict MaVI status in HCC, meanwhile explore the prognostic prediction power of the radiomics model. A cohort of 452 patients diagnosed with HCC was collected from 5 hospitals in China with complete CT images, clinical data, and follow-ups. 15 out of 708 radiomic features were selected for MaVI prediction using LASSO regression modeling. A radiomics signature was constructed by support vector machine based on the 15 selected features. To evaluate the prognostic power of the signature, Kaplan-Meier curves with log-rank test were plotted on MaVI occurrence time (MOT), progression free survival (PFS) and overall survival (OS). The radiomics signature showed satisfactory performance on MaVI prediction with area under curves of 0.885 and 0.770 on the training and external validation cohorts, respectively. Patients could successfully be divided into high- and low-risk groups on MOT and PFS with p-value of 0.0017 and 0.0013, respectively. Regarding to OS, the Kaplan-Meier curve did not present with significant difference which may be caused by non-uniform following treatments after disease progression. To conclude, the proposed radiomics model could facilitate MaVI prediction along with prognostic implication in HCC management.
Prostate specific antigen (PSA) screening is routinely conducted for suspected prostate cancer (PCa) patients. As this technique might result in high probability of over-diagnosis and unnecessary prostate biopsies, controversies on it remains especially for patients with “gray-zone” PSA levels, i.e. 4-10ng/ml. To improve the risk stratification of suspected PCa patients, Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) was released in 2015. Although PI-RADSv2 showed good performance in the detection of PCa, its specificity was relatively low for patients with gray-zone PSA levels. This indicated that over-diagnosis issue could not be dealt well by PI-RADSv2 in the gray zone. Addressing this, we attempted to validate whether radiomics analysis of Diffusion weighted Imaging (DWI) data could reduce over-diagnosis of PCa with gray-zone PSA levels. Here, 140 suspected PCa patients in Peking Union Medical College Hospital were enrolled. 700 radiomic features were extracted from the DWI data. Least absolute shrinkage and selection operator (LASSO) were conducted, and 7 radiomic features were selected on the training set (n=93). Based on these features, random forest classifier was used to build the Radiomics model, which performed better than PI-RADSv2 (area under the curve [AUC]: 0.900 vs 0.773 and 0.844 vs 0.690 on the training and test sets). Furthermore, the specificity values of Radiomics model and PI-RADSv2 was 0.815 and 0.481 on the test set, respectively. In conclusion, radiomics analysis of DWI data might reduce the over-diagnosis of PCa with gray-zone PSA levels.
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