Sensitivity analysis of FDG PET tumor voxel cluster radiomics and dosimetry for predicting mid-chemoradiation regional response of locally advanced lung cancer

Novel Functional Radiomics for Prediction of Cardiac Positron Emission Tomography Avidity in Lung Cancer Radiotherapy

PURPOSE

Traditional methods of evaluating cardiotoxicity focus on radiation doses to the heart. Functional imaging has the potential to provide improved prediction for cardiotoxicity for patients with lung cancer. Fluorine-18 (18F) fluorodeoxyglucose (FDG)-positron emission tomography (PET)/computed tomography (CT) imaging is routinely obtained in a standard cancer staging workup. This work aimed to develop a radiomics model predicting clinical cardiac assessment using 18F-FDG PET/CT scans before thoracic radiation therapy.

METHODS

Pretreatment 18F-FDG PET/CT scans from three study populations (N = 100, N = 39, N = 70) were used, comprising two single-institutional protocols and one publicly available data set. A clinician (V.J.) classified the PET/CT scans per clinical cardiac guidelines as no uptake, diffuse uptake, or focal uptake. The heart was delineated, and 210 novel functional radiomics features were selected to classify cardiac FDG uptake patterns. Training data were divided into training (80%)/validation (20%) sets. Feature reduction was performed using the Wilcoxon test, hierarchical clustering, and recursive feature elimination. Ten-fold cross-validation was carried out for training, and the accuracy of the models to predict clinical cardiac assessment was reported.

RESULTS

From 202 of 209 scans, cardiac FDG uptake was scored as no uptake (39.6%), diffuse uptake (25.3%), and focal uptake (35.1%), respectively. Sixty-two independent radiomics features were reduced to nine clinically pertinent features. The best model showed 93% predictive accuracy in the training data set and 80% and 92% predictive accuracy in two external validation data sets.

CONCLUSION

This work used an extensive patient data set to develop a functional cardiac radiomic model from standard-of-care 18F-FDG PET/CT scans, showing good predictive accuracy. The radiomics model has the potential to provide an automated method to predict existing cardiac conditions and provide an early functional biomarker to identify patients at risk of developing cardiac complications after radiotherapy.

The critical components for effective adaptive radiotherapy in patients with unresectable non-small-cell lung cancer: who, when and how

Adaptive radiotherapy (ART) is a new radiotherapy technology based on image-guided radiation therapy technology, used to avoid radiation overexposure to residual tumors and the surrounding normal tissues. Tumors undergoing the same radiation doses and modes can occur unequal shrinkage due to the variation of response times to radiation doses in different patients. To perform ART effectively, eligible patients with a high probability of benefits from ART need to be identified. Confirming the precise timetable for ART in every patient is another urgent problem to be resolved. Moreover, the outcomes of ART are different depending on the various image guidance used. This review discusses 'who, when and how' as the three key factors involved in the most effective implementation for the management of ART.

Non-Contrast CT-Based Radiomics Score for Predicting Hematoma Enlargement in Spontaneous Intracerebral Hemorrhage

PURPOSE

To develop a non-contrast computed tomography-(CT)-based radiomics score for predicting the risk of hematoma early enlargement in spontaneous intracerebral hemorrhage.

METHODS

A total of 258 patients from a single-center database with acute spontaneous intracerebral parenchymal hemorrhage were collected. Radiomics software was explored to segment hematomas on baseline non-contrast CT images, and the texture features were extracted. Minimal Redundancy and Maximal Relevance (mRMR) and Least Absolute Shrinkage and Selection Operator (LASSO), were used to select optimized subset of features and radiomics score was calculated. The radiomics model (radiomics score-based), radiomics nomogram (radiomics score combined with clinical factors-based) and clinical model (clinical factors-based) were built in a training cohort and validated in a test cohort. The discrimination, calibration, and clinical usefulness of the models were evaluated. Finally, a subgroup analysis was performed to assess the predictive value of radiomics score in specific hemorrhage location.

RESULTS

Radiomics score was composed of 12 radiomics features. The radiomics model and radiomics nomogram both showed good performance in predicting hematoma enlargement (area under the curve, AUC 0.83 [0.71-0.95], AUC 0.82 [0.72, 0.93]), and were both better than clinical model (AUC 0.66 [0.54-0.79]). The radiomics model and radiomics nomogram showed satisfactory calibration and clinical usefulness for detecting hematoma enlargement. For subgroup analysis, radiomics score also showed good predictive value for hematoma enlargement in different locations (AUC were 0.828, 0.940, 0.836 and 0.904, respectively, for supratentorial, subtentorial, deep and lobes).

CONCLUSION

A radiomics score based on non-contrast CT may be considered as a potential biomarker for prediction of hematoma enlargement in patients with spontaneous intracerebral hemorrhage (SICH), and it presented a high incremental value to clinical factors for hematoma enlargement prediction.

Value of PET imaging for radiation therapy

This comprehensive review written by experts in their field gives an overview on the current status of incorporating positron emission tomography (PET) into radiation treatment planning. Moreover, it highlights ongoing studies for treatment individualisation and per-treatment tumour response monitoring for various primary tumours. Novel tracers and image analysis methods are discussed. The authors believe this contribution to be of crucial value for experts in the field as well as for policy makers deciding on the reimbursement of this powerful imaging modality.

Value of PET imaging for radiation therapy

This comprehensive review written by experts in their field gives an overview on the current status of incorporating positron emission tomography (PET) into radiation treatment planning. Moreover, it highlights ongoing studies for treatment individualisation and per-treatment tumour response monitoring for various primary tumours. Novel tracers and image analysis methods are discussed. The authors believe this contribution to be of crucial value for experts in the field as well as for policy makers deciding on the reimbursement of this powerful imaging modality.