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.

This study developed a radiomics-based predictive model for radiation-induced pneumonitis (RP) after lung cancer stereotactic body radiation therapy (SBRT) on pretreatment planning computed tomography (CT) images. For the RP prediction models, 275 non-small-cell lung cancer patients consisted of 245 training (22 with grade ≥ 2 RP) and 30 test cases (8 with grade ≥ 2 RP) were selected. A total of 486 radiomic features were calculated to quantify the RP texture patterns reflecting radiation-induced tissue reaction within lung volumes irradiated with more than x Gy, which were defined as LVx. Ten subsets consisting of all 22 RP cases and 22 or 23 randomly selected non-RP cases were created from the imbalanced dataset of 245 training patients. For each subset, signatures were constructed, and predictive models were built using the least absolute shrinkage and selection operator logistic regression. An ensemble averaging model was built by averaging the RP probabilities of the 10 models. The best model areas under the receiver operating characteristic curves (AUCs) calculated on the training and test cohort for LV5 were 0.871 and 0.756, respectively. The radiomic features calculated on pretreatment planning CT images could be predictive imaging biomarkers for RP after lung cancer SBRT.

No abstract

Objectives To propose a novel robust radiogenomics approach to the identification of epidermal growth factor receptor (EGFR) mutations among patients with non-small cell lung cancer (NSCLC) using Betti numbers (BNs). Materials and methods Contrast enhanced computed tomography (CT) images of 194 multi-racial NSCLC patients (79 EGFR mutants and 115 wildtypes) were collected from three different countries using 5 manufacturers’ scanners with a variety of scanning parameters. Ninety-nine cases obtained from the University of Malaya Medical Centre (UMMC) in Malaysia were used for training and validation procedures. Forty-one cases collected from the Kyushu University Hospital (KUH) in Japan and fifty-four cases obtained from The Cancer Imaging Archive (TCIA) in America were used for a test procedure. Radiomic features were obtained from BN maps, which represent topologically invariant heterogeneous characteristics of lung cancer on CT images, by applying histogram- and texture-based feature computations. A BN-based signature was determined using support vector machine (SVM) models with the best combination of features that maximized a robustness index (RI) which defined a higher total area under receiver operating characteristics curves (AUCs) and lower difference of AUCs between the training and the validation. The SVM model was built using the signature and optimized in a five-fold cross validation. The BN-based model was compared to conventional original image (OI)- and wavelet-decomposition (WD)-based models with respect to the RI between the validation and the test. Results The BN-based model showed a higher RI of 1.51 compared with the models based on the OI (RI: 1.33) and the WD (RI: 1.29). Conclusion The proposed model showed higher robustness than the conventional models in the identification of EGFR mutations among NSCLC patients. The results suggested the robustness of the BN-based approach against variations in image scanner/scanning parameters.

Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). AbstractPurpose: Fragility hip fractures (FHFs) are associated with a high risk of mortality, but the relative contribution of various factors remains controversial. This study aimed to evaluate predictive factors of mortality at 1 year after discharge in Japan. Methods: A total of 497 patients aged 60 years or older who sustained FHFs during follow-up were included in this study. Expected variables were finally assessed using multivariable Cox proportional hazards models. Results: The 1-year mortality rate was 9.1% (95% confidence interval: 6.8-12.0%, n ¼ 45). Log-rank test revealed that previous fractures (p ¼ 0.003), Barthel index (BI) at discharge (p ¼ 0.011), and place-to-discharge (p ¼ 0.004) were significantly associated with mortality for male patients. Meanwhile, body mass index (BMI; p ¼ 0.023), total Charlson comorbidity index (TCCI; p ¼ 0.005), smoking (p ¼ 0.007), length of hospital stay (LOS; p ¼ 0.009), and BI (p ¼ 0.004) were the counterparts for females. By multivariate analyses, previous vertebral fractures (hazard ratio (HR) 3.33; p ¼ 0.044), and BI <30 (HR 5.42, p ¼ 0.013) were the predictive variables of mortality for male patients. BMI <18.5 kg/m 2 (HR 2.70, p ¼ 0.023), TCCI 5 (HR 2.61, p ¼ 0.032), smoking history (HR 3.59, p ¼ 0.018), LOS <14 days (HR 13.9; p ¼ 0.007), and BI <30 (HR 2.76; p ¼ 0.049) were the counterparts for females. Conclusions: Previous vertebral fractures and BI <30 were the predictive variables of mortality for male patients, and BMI <18.5 kg/m 2 , TCCI 5, smoking history, LOS <14 days, and BI <30 were those for females. Decreased BI is one of the independent and preventable risk factors. A comprehensive therapeutic approach should be considered to prevent deterioration of activities of daily living and a higher risk of mortality.