Abdallah Damin Abukhalil, Mai Yousef, Marwa Ammar, Weam Jaghama, Ni'meh Al-Shami, Hani A Naseef, Abdullah K Rabba
Practices, Efficacy, and Reported Side Effects Associated with Isotretinoin Treatment in Palestine
Patient preference and adherence
differentiating benign malignant sacral tumors crucial determining appropriate treatment options study aims develop two benchmark fusion models deep learning radiomic nomogram dlrn capable distinguishing benign malignant sacral tumors using multiple imaging modalities reviewed axial t2weighted imaging t2wi noncontrast computed tomography ncct 134 patients pathologically confirmed sacral tumors two benchmark fusion models developed using fusion deep learning dl features fusion classical machine learning cml features multiple imaging modalities employing logistic regression knearest neighbor classification extremely randomized trees two benchmark models exhibiting robust predictive performance merged clinical data formulate dlrn performance assessment involved computing area receiver operating characteristic curve auc sensitivity specificity accuracy negative predictive value npv positive predictive value ppv dl benchmark fusion model demonstrated superior performance compared cml fusion model dlrn identified optimal model exhibited highest predictive performance achieving accuracy 0889 auc 0961 test sets calibration curves utilized evaluate predictive capability models decision curve analysis dca conducted assess clinical net benefit dlr model dlrn could serve practical predictive tool capable distinguishing benign malignant sacral tumors offering valuable information risk counseling aiding clinical treatment decisions