Multiparametric MRI-based radiomics analysis: differentiation of subtypes of cervical cancer in the early stage.

BACKGROUND: There are significant differences in outcomes for different histological subtypes of cervical cancer (CC). Yet, it is difficult to distinguish CC subtypes using non-invasive methods. PURPOSE: To investigate whether multiparametric magnetic resonance imaging (MRI)-based radiomics analysis can differentiate CC subtypes and explore tumor heterogeneity. MATERIAL AND METHODS: This study retrospectively analyzed 96 patients with CC (squamous cell carcinoma [SCC] = 50, adenocarcinoma [AC] = 46) who underwent pelvic MRI before surgery. Radiomics features were extracted from the tumor volumes on five sequences (sagittal T2-weighted imaging [T2SAG], transverse T2-weighted imaging [T2TRA], sagittal contrast-enhanced T1-weighted imaging [CESAG], transverse contrast-enhanced T1-weighted imaging [CETRA], and apparent diffusion coefficient [ADC]). Clustering and logistic regression were used to examine the distinguishing capabilities of radiomics features extracted from five different MR sequences. RESULTS: Among the 105 extracted radiomics features, there were 51, 38, 37, and 2 features that showed intergroup differences for T2SAG, T2TRA, ADC, and CESAG, respectively (all P < 0.05). AC had greater textural heterogeneity than SCC (P < 0.05). Upon unsupervised clustering of significantly different features, T2SAG achieved the highest accuracy (0.844; sensitivity = 0.920; specificity = 0.761). The largest area under the curve (AUC) for classification ability was 0.86 for T2SAG. Hence, the radiomics model from five combined MR sequences (AUC = 0.89; accuracy = 0.81; sensitivity = 0.67; specificity = 0.94) exhibited better differentiation ability than any MR sequence alone. CONCLUSION: Multiparametric MRI-based radiomics models may be a promising method to differentiate AC and SCC. AC showed more heterogeneous features than SCC.

Imaging-Based Individualized Response Prediction Of Carbon Ion Radiotherapy For Prostate Cancer Patients.

PURPOSE: To explore the value of the pre-treatment MRI radiomic features in individualized prediction of the therapeutic response of carbon ion radiotherapy (CIRT) for prostate cancer patients. PATIENTS AND METHODS: Twenty-three patients with localized prostate cancer treated by CIRT were enrolled for analysis. Prostate tumors were manually delineated on T2-weighted (T2w) images and apparent diffusion coefficient (ADC) maps acquired before CIRT. Abundant radiomic features were extracted from the delineations, which were randomly deformed to account for delineation uncertainty. The robust features were selected and then compared between patient groups of different CIRT responses. Support vector machine (SVM) was subsequently applied to demonstrate the role of the radiomic features to predict individualized CIRT response in the way of artificial intelligence. RESULTS: Radiomic features from ADC had significantly higher intra-correlation coefficient (ICC) (0.71±0.28) than T2w features (0.60±0.31) (p<0.01), indicating higher robustness of ADC features against delineation uncertainty. More features were excellently robust in ADC (58.2% of all the radiomic feature candidates, compared to 41.3% in T2w). By combining the excellently robust radiomic features of T2w and ADC, SVM achieved high performance to predict individualized therapeutic response of CIRT, ie, area-under-curve (AUC) = 0.88. CONCLUSION: Radiomic features extracted from T2w and ADC images displayed great robustness to quantify the tumor characteristics of prostate cancer and high accuracy to predict the individualized therapeutic response of CIRT. After further validation, the selected radiomic features may become potential imaging biomarkers in the management of prostate cancer through CIRT.

Quantitative susceptibility mapping based hybrid feature extraction for diagnosis of Parkinson's disease.

Parkinson's disease is the second most common neurodegenerative disease in the elderly after Alzheimer's disease. The aetiology and pathogenesis of Parkinson's disease (PD) are still unclear, but the loss of dopaminergic cells and the excessive iron deposition in the substantia nigra (SN) are associated with the pathophysiology. As an imaging technique that can quantitatively reflect the amount of iron deposition, Quantitative Susceptibility Mapping (QSM) has been shown to be a promising modality for the diagnosis of PD. In the present work, we propose a hybrid feature extraction method for PD diagnosis using QSM images. First, we extract radiomics features from the SN using QSM and employ machine learning algorithms to classify PD and normal controls (NC). This approach allows us to investigate which features are most vulnerable to the effects of the disease. Along with this approach, we propose a Convolutional Neural Network (CNN) based method which can extract different features from the QSM image to further support the diagnosis of PD. Finally, we combine these two types of features and we find that the radiomics features and CNN features are complementary to each other, which helps further improve the classification (diagnostic) performance. We conclude that: (1) radiomics features from QSM data have significant clinical value for the diagnosis of PD; (2) CNN features are also useful in the diagnosis of PD; and (3) the combination of radiomics features and CNN features can enhance the diagnostic accuracy.

Can pretreatment 18F-FDG PET tumor texture features predict the outcomes of osteosarcoma treated by neoadjuvant chemotherapy?

PURPOSE: To investigate whether tumor texture features derived from pretreatment with 18F-fluorodeoxyglucose positron emission tomography (FDG PET) can predict histological response or event-free survival (EFS) in patients with localized osteosarcoma of the extremities treated by neoadjuvant chemotherapy (NAC). METHODS: We retrospectively reviewed 35 patients with American Joint Committee on Cancer stage II extremity osteosarcoma treated with NAC and surgery. Primary tumor traditional parameters and texture features were measured for all 18F-FDG PET images prior to treatment. After surgery, histological responses to NAC were evaluated on the postsurgical specimens. A receiver operating characteristic curve (ROC) was constructed to evaluate the optimal predictive performance among the various indices. EFS was calculated using the Kaplan-Meier method and prognostic significance was assessed by Cox proportional hazards analysis. RESULTS: Pathologic examination revealed 16 (45.71%) good responders and 19 (54.29%) poor responders. Although both the texture features (least axis, dependence nonuniformity, run length nonuniformity, and size zone nonuniformity) and metabolic tumor volume (MTV) can predict tumor response of osteosarcoma to NAC, the traditional indicator MTV has the best performance according to ROC curve analysis (area under the curve = 0.918, p < 0.0001). In multivariate analysis, MTV (p < 0.0001), histological response (p = 0.0003), and texture feature of coarsenessNGTDM (neighboring gray tone difference matrix) (p = 0.005) were independently associated with EFS. CONCLUSIONS: Intratumoral heterogeneity of baseline 18F-FDG uptake measured by PET texture analysis can predict tumor response and EFS of patients with extremity osteosarcoma treated by NAC, but the conventional parameter MTV provides better predictive power and is a strong independent prognostic factor. KEY POINTS: • The baseline 18 F-FDG PET tumor texture features can predict tumor NAC response for patients with osteosarcoma. • Coarseness NGTDM is a new and independent prognostic factor for osteosarcoma. • MTV provides the best predictive power and is a strong independent prognostic factor for patients with osteosarcoma.