Sellami, S.; Bourbonne, V.; Hatt, M.; Tixier, F.; Bouzid, D.; Lucia, F.; Pradier, O.; Goasduff, G.; Visvikis, D.; Schick, U. published the article 《Predicting response to radiotherapy of head and neck squamous cell carcinoma using radiomics from cone-beam CT images》. Keywords: head and neck squamous cell carcinoma radiotherapy radiomics human; CBCT; head and neck cancers; radiomics; radiotherapy; texture.They researched the compound: cis-Diammine(1,1-cyclobutanedicarboxylato)platinum(II)( cas:41575-94-4 ).SDS of cas: 41575-94-4. Aromatic heterocyclic compounds can be divided into two categories: single heterocyclic and fused heterocyclic. In addition, there is a lot of other information about this compound (cas:41575-94-4) here.
Radiotherapy (RT) for head and neck cancer is now guided by cone-beam computed tomog. (CBCT). We aim to identify a CBCT radiomic signature predictive of progression to RT. A cohort of 93 patients was split into training (n = 60) and testing (n = 33) sets. A total of 88 features were extracted from the gross tumor volume (GTV) on each CBCT. Receiver operating characteristic (ROC) curves were used to determine the power of each feature at each week of treatment to predict progression to radio(chemo)therapy. Only features with AUC > 0.65 at each week were pre-selected. Absolute differences were calculated between features from each weekly CBCT and baseline CBCT1 images. The smallest detectable change (C = 1.96 x SD, SD being the standard deviation of differences between feature values calculated on CBCT1 and CBCTn) with its confidence interval (95% confidence interval [CI]) was determined for each feature. The features for which the change was larger than C for at least 5% of patients were then selected. A radiomics-based model was built at the time-point that showed the highest AUC and compared with models relying on clin. variables. Seven features had an AUC > 0.65 at each week, and six exhibited a change larger than the predefined CI 95%. After exclusion of inter-correlated features, only one parameter remains, Coarseness. Among clin. variable, only Hb value was significant. AUC for predicting the treatment response were 0.78 (p = .006), 0.85 (p < .001), and 0.99 (p < .001) for clin., CBCT4-radiomics (Coarseness) and clin. + radiomics based models resp. The mean AUC of this last model on a 5-fold cross-validation was 0.80 (±0.09). On the testing cohort, the best prediction was given by the combined model (balanced accuracy [BAcc] 0.67 , p < .001). We described a feature selection methodol. for delta-radiomics that is able to select reproducible features which are informative due to their change during treatment. A selected delta radiomics feature may improve clin.-based prediction models. Compound(41575-94-4)SDS of cas: 41575-94-4 received a lot of attention, and I have introduced some compounds in other articles, similar to this compound(cis-Diammine(1,1-cyclobutanedicarboxylato)platinum(II)), if you are interested, you can check out my other related articles.
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