Recently, the concept of radiomics has emerged from radiation oncology. It is a novel approach for solving the issues of precision medicine and how it can be performed, based on multimodality medical images that are non-invasive, fast and low in cost. Radiomics is the comprehensive analysis of massive numbers of medical images in order to extract a large number of phenotypic features (radiomic biomarkers) reflecting cancer traits, and it explores the associations between the features and patients’ prognoses in order to improve decision-making in precision medicine. Individual patients can be stratified into subtypes based on radiomic biomarkers that contain information about cancer traits that determine the patient’s prognosis. Machine-learning algorithms of AI are boosting the powers of radiomics for prediction of prognoses or factors associated with treatment strategies, such as survival time, recurrence, adverse events, and subtypes. Therefore, radiomic approaches, in combination with AI, may potentially enable practical use of precision medicine in radiation therapy by predicting outcomes and toxicity for individual patients.

We have investigated an approach for prediction of parotid gland tumor (PGT) malignancy on preoperative magnetic resonance (MR) images. The PGT regions were segmented on the MR images of 42 patients. A total of 972 radiomic features were extracted from tumor regions in T1- and T2-weighted MR images. Five features were selected as a radiomic biomarker from the 972 features by using a least absolute shrinkage and selection operator (LASSO). Malignancies of PGTs (high grade versus intermediate and low grades) were predicted by using random forest (RF) and k-nearest neighbors (k-NN) with the radiomic biomarker. The proposed approach was evaluated using the accuracy and the mean area under the receiver operating characteristic curve (AUC) based on a leave-one-out cross validation test. The accuracy and AUC of the malignancy prediction of PGTs were 73.8% and 0.88 for the RF and 88.1% and 0.95 for the k-NN, respectively. Our results suggested that the radiomics-based k-NN approach using preoperative MR images could be feasible to predict the malignancy of PGT.

The purpose of this study was to investigate a practical patient dose index for assessing the patient organ dose from a cone-beam computed tomography (CBCT) scan by comparing eight dose indices, i.e. CTDI₁₀₀, CTDI_IEC, CTDI_∞, midpoint doses f(0)_PMMA for a cylindrical polymethyl methacrylate (PMMA) phantom, f(0)_Ap for an anthropomorphic phantom and f(0)_Pat for a prostate cancer patient, as well as the conventional size specific dose estimations (SSDE_conv) and modified SSDE (SSDE_mod), with organ dose for the prostate (OD_prost) obtained via Monte Carlo (MC) simulation. The OD_prost was the reference dose used to find the practical dose index at the center of the pelvic region of a prostate cancer patient. The smallest error rate with respect to the OD_prost of 19.3 mGy (reference) among eight dose indices was 5% for f(0)_Pat. The practical patient dose index was the f(0)_Pat, which showed the smallest error with respect to the reference dose.

We investigated an approach to predict treatment failure after chemoradiation using radiomics and dosiomics in patients with pharyngeal squamous cell carcinoma (PSCC). Data of 172 patients were collected from a public database. The cases were divided into the training (n = 140) and testing (n = 32) datasets. A total of 1027 features, including radiomic (R) features (first-order, texture, and wavelet features), local binary pattern-based (L) features, and topological (T) features, were extracted from the computed tomography (CT) image and dose distribution (DD) of the gross tumor volume. The Coxnet algorithm was employed on the training dataset to select significant features. Twenty-One treatment failure prediction models were constructed based on Rad scores. The overall adequacy of the treatment failure prediction models was evaluated using the concordance index (C-index) and statistically significant differences (p-values) between the Kaplan–Meier curves of the two risk groups. The DD-based LT (DD-LT) model and the combined CT with DD-based RLT (CD-RLT) model showed statistically significant differences in the Kaplan–Meier curves. The C-indices were 0.74 for the DD-LT model and 0.64, the CD-RLT model. These models exhibited higher performance than the conventional approach. The proposed radiomics/dosiomics-based model may be more accurate in predicting treatment failure after chemoradiation in patients with PSCC.

Predicting treatment failure (TF) in head-and-neck squamous cell carcinoma (HNSCC) patients before treatment can help in selecting a more appropriate treatment approach. We investigated a novel radiodosiomics approach to predict TF prior to chemoradiation in HNSCC patients. Computed tomography (CT) images, dose distributions (DDs), and clinical data from 172 cases were collected from a public database. The cases were divided into the training (n = 140) and testing (n = 32) datasets. A total of 1027 features, including conventional radiomic (R) features, local binary pattern-based (L) features, and topological (T) features, were extracted from the CT images and DDs of the tumor region. Moreover, deep (D) features were extracted from a deep learning-based prediction model. The Coxnet algorithm was employed to select significant features. Twenty-two treatment failure prediction models were constructed based on Rad-scores. TF prediction models were assessed using the concordance index (C-index) and statistically significant variations in the Kaplan–Meier curves between the two risk groups. The Kaplan–Meier curves of the DD-based T (DD-T) model displayed statistically significant differences. The highest C-index of the testing dataset for this model was 0.760. The proposed radiodosiomics models could potentially demonstrate greater accuracy in anticipating TF before chemoradiation in HNSCC patients.