Muscle loss after radiotherapy is associated with poorer survival in patients with oral cavity squamous cell carcinoma (OCSCC). However, the threshold of muscle loss remains unclear. This study aimed to utilize explainable artificial intelligence to identify the threshold of muscle loss associated with survival in OCSCC. We enrolled 1087 patients with OCSCC treated with surgery and adjuvant radiotherapy at two tertiary centers (660 in the derivation cohort and 427 in the external validation cohort). Skeletal muscle index (SMI) was measured using pre- and post-radiotherapy computed tomography (CT) at the C3 vertebral level. Random forest (RF), eXtreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost) models were developed to predict all-cause mortality, and their performances were evaluated using the area under the curve (AUC). Muscle loss threshold was identified using the SHapley Additive exPlanations (SHAP) method and validated using Cox regression analysis. In the external validation cohort, the RF, XGBoost, and CatBoost models achieved favorable performance in predicting all-cause mortality (AUC: 0.898, 0.859, and 0.842). The SHAP method demonstrated that SMI change after radiotherapy was the most important feature for predicting all-cause mortality and consistently identified SMI loss ≥ 4.2% as the threshold in all three models. In multivariable analysis, SMI loss ≥ 4.2% was independently associated with increased all-cause mortality risk in both cohorts (derivation cohort: hazard ratio: 6.66, p < 0.001; external validation cohort: hazard ratio: 8.46, p < 0.001). This study can assist clinicians in identifying patients with considerable muscle loss after treatment and guide interventions to improve muscle mass. Question Muscle loss after radiotherapy is associated with poorer survival in patients with oral cavity cancer; however, the threshold of muscle loss remains unclear. Findings Explainable artificial intelligence identified muscle loss ≥ 4.2% as the threshold of increased all-cause mortality risk in both derivation and external validation cohorts. Clinical Relevance Muscle loss ≥ 4.2% may be the optimal threshold for survival in patients who receive adjuvant radiotherapy for oral cavity cancer. This threshold can guide clinicians in improving muscle mass after radiotherapy.

To construct a predictive model using deep-learning radiomics and clinical risk factors for assessing the preoperative histopathological grade of bladder cancer according to computed tomography (CT) images. A retrospective analysis was conducted involving 201 bladder cancer patients with definite pathological grading results after surgical excision at the organization between January 2019 and June 2023. The cohort was classified into a test set of 81 cases and a training set of 120 cases. Hand-crafted radiomics (HCR) and features derived from deep-learning (DL) were obtained from computed tomography (CT) images. The research builds a prediction model using 12 machine-learning classifiers, which integrate HCR, DL features, and clinical data. Model performance was estimated utilizing decision-curve analysis (DCA), the area under the curve (AUC), and calibration curves. Among the classifiers tested, the logistic regression model that combined DL and HCR characteristics demonstrated the finest performance. The AUC values were 0.912 (training set) and 0.777 (test set). The AUC values of clinical model achieved 0.850 (training set) and 0.804 (test set). The AUC values of the combined model were 0.933 (training set) and 0.824 (test set), outperforming both the clinical and HCR-only models. The CT-based combined model demonstrated considerable diagnostic capability in differentiating high-grade from low-grade bladder cancer, serving as a valuable noninvasive instrument for preoperative pathological evaluation.

Predicting pathological complete response (pCR) from pre or post-treatment features could be significant in improving the process of making clinical decisions and providing a more personalized treatment approach for better treatment outcomes. However, the lack of external validation of predictive models, missing in several published articles, is a major issue that can potentially limit the reliability and applicability of predictive models in clinical settings. Therefore, this systematic review described different externally validated methods of predicting response to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) patients and how they could improve clinical decision-making. An extensive search for eligible articles was performed on PubMed, Cochrane, and Scopus between 2018 and 2023, using the keywords: (Response OR outcome) prediction AND (neoadjuvant OR chemoradiotherapy) treatment in 'locally advanced Rectal Cancer'. (i) Studies including patients diagnosed with LARC (T3/4 and N- or any T and N+) by pre-medical imaging and pathological examination or as stated by the author (ii) Standardized nCRT completed. (iii) Treatment with long or short course radiotherapy. (iv) Studies reporting on the prediction of response to nCRT with pathological complete response (pCR) as the primary outcome. (v) Studies reporting external validation results for response prediction. (vi) Regarding language restrictions, only articles in English were accepted. (i) We excluded case report studies, conference abstracts, reviews, studies reporting patients with distant metastases at diagnosis. (ii) Studies reporting response prediction with only internally validated approaches. Three researchers (DC-D, FB, HT) independently reviewed and screened titles and abstracts of all articles retrieved after de-duplication. Possible disagreements were resolved through discussion among the three researchers. If necessary, three other researchers (LB, GC, MG) were consulted to make the final decision. The extraction of data was performed using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) template and quality assessment was done using the Prediction model Risk Of Bias Assessment Tool (PROBAST). A total of 4547 records were identified from the three databases. After excluding 392 duplicate results, 4155 records underwent title and abstract screening. Three thousand and eight hundred articles were excluded after title and abstract screening and 355 articles were retrieved. Out of the 355 retrieved articles, 51 studies were assessed for eligibility. Nineteen reports were then excluded due to lack of reports on external validation, while 4 were excluded due to lack of evaluation of pCR as the primary outcome. Only Twenty-eight articles were eligible and included in this systematic review. In terms of quality assessment, 89 % of the models had low concerns in the participants domain, while 11 % had an unclear rating. 96 % of the models were of low concern in both the predictors and outcome domains. The overall rating showed high applicability potential of the models with 82 % showing low concern, while 18 % were deemed unclear. Most of the external validated techniques showed promising performances and the potential to be applied in clinical settings, which is a crucial step towards evidence-based medicine. However, more studies focused on the external validations of these models in larger cohorts is necessary to ensure that they can reliably predict outcomes in diverse populations.

The purpose of this study is to objectively quantify the degree of overcorrection in our current practice and to evaluate longitudinal morphological changes using CranioRate^TM, a novel machine learning skull morphology assessment tool.  Design:Retrospective cohort study across multiple time points. Tertiary care children's hospital. Patients with preoperative and postoperative CT scans who underwent fronto-orbital advancement (FOA) for metopic craniosynostosis. We evaluated preoperative, postoperative, and two-year follow-up skull morphology using CranioRate^TM to generate a Metopic Severity Score (MSS), a measure of degree of metopic dysmorphology, and Cranial Morphology Deviation (CMD) score, a measure of deviation from normal skull morphology. Fifty-five patients were included, average age at surgery was 1.3 years. Sixteen patients underwent follow-up CT imaging at an average of 3.1 years. Preoperative MSS was 6.3 ± 2.5 (CMD 199.0 ± 39.1), immediate postoperative MSS was -2.0 ± 1.9 (CMD 208.0 ± 27.1), and longitudinal MSS was 1.3 ± 1.1 (CMD 179.8 ± 28.1). MSS approached normal at two-year follow-up (defined as MSS = 0). There was a significant relationship between preoperative MSS and follow-up MSS (R²= 0.70). MSS quantifies overcorrection and normalization of head shape, as patients with negative values were less "metopic" than normal postoperatively and approached 0 at 2-year follow-up. CMD worsened postoperatively due to postoperative bony changes associated with surgical displacements following FOA. All patients had similar postoperative metopic dysmorphology, with no significant association with preoperative severity. More severe patients had worse longitudinal dysmorphology, reinforcing that regression to the metopic shape is a postoperative risk which increases with preoperative severity.

Traditional clinical diagnostic methods of rapid eye movement sleep behavior disorder (RBD) have certain limitations, especially in the early stages. This study aims to develop and validate an magnetic resonance imaging (MRI) radiomics-based machine learning classifier to accurately detect RBD patients with Parkinson's disease (PD). Data from 183 subjects, including 63 PD patients with RBD, sourced from the PPMI database were utilized in this study. The data were randomly divided into training (70%) and testing (30%) sets. Quantitative radiomic features of white matter, gray matter, and cerebrospinal fluid were extracted from whole-brain structural MRI images. Feature reduction was performed on the training set data to construct radiomics signatures. Additionally, multi-factor logistic regression analysis identified clinical predictors associated with PD-RBD, and these clinical features were integrated with the radiomics signatures to develop predictive models using various machine learning algorithms. The model exhibiting the best performance was selected, and receiver operating characteristic (ROC) curves were used to evaluate its performance in both the training and testing sets. Furthermore, based on the optimal cut-off value of the model, subjects were categorized into low- and high-risk groups, and differences in the actual number of RBD patients between the two sets were compared to assess the clinical effectiveness of the model. The radiomics signatures achieved areas under the curve (AUC) of 0.754 and 0.707 in the training and testing sets, respectively. Multi-factor logistic regression analysis revealed that postural instability was an independent predictor of PD-RBD. The random forest model, which integrated radiomics signatures with postural instability, demonstrated superior performance in predicting PD-RBD. Specifically, its AUCs in the training and testing sets were 0.917 and 0.882, with sensitivities of 0.933 and 0.889, and specificities of 0.786 and 0.722, respectively. Based on the optimal cut-off value of 0.3772, significant differences in the actual number of PD-RBD patients were observed between low-risk and high-risk groups in both the training and testing sets (P < 0.05). MRI-based radiomic signatures have the potential to serve as biomarkers for PD-RBD. The random forest model, which integrates radiomic signatures with postural instability, and shows improved performance in identifying PD-RBD. This approach offers valuable insights for prognostic evaluation and preventive treatment strategies.

To improve the prediction of immune checkpoint inhibitors (ICIs) efficacy in hepatocellular carcinoma (HCC), this study categorized the tumor immune microenvironment (TIME) into two types: immune-activated (IA), characterized by a high CD8 + score and high PD-L1 combined positive score (CPS), and non-immune-activated (NIA), encompassing all other conditions. We aimed to develop an MRI-based radiomics model to predict TIME types and validate its predictive capability for ICIs efficacy in HCC patients receiving anti-PD-1/PD-L1 therapy. The study included 200 HCC patients who underwent preoperative/pretreatment multiparametric contrast-enhanced MRI (Cohort 1: 168 HCC patients with hepatectomy from two centres; Cohort 2: 42 advanced HCC patients on anti-PD-1/PD-L1 therapy). In Cohort 1, after feature selection, clinical, intratumoral radiomics, peritumoral radiomics, combined radiomics, and clinical-radiomics models were established using machine learning algorithms. In cohort 2, the clinical-radiomics model's predictive ability for ICIs efficacy was assessed. In Cohort 1, the AUC values for intratumoral, peritumoral, and combined radiomics models were 0.825, 0.809, and 0.868, respectively, in the internal validation set, and 0.73, 0.759, and 0.822 in the external validation set; the clinical-radiomics model incorporating neutrophil-to-lymphocyte ratio, tumor size, and combined radiomics score achieved an AUC of 0.887 in the internal validation set, outperforming clinical model (P = 0.049), and an AUC of 0.837 in the external validation set. In cohort 2, the clinical-radiomics model stratified patients into low- and high-score groups, demonstrating a significant difference in objective response rate (p = 0.003) and progression-free survival (p = 0.031). The clinical-radiomics model is effective in predicting TIME types and efficacy of ICIs in HCC, potentially aiding in treatment decision-making.

Given that resection of brainstem cavernous malformations (BSCMs) ends hemorrhaging but carries a high risk of neurological deficits, it is necessary to develop and validate a model predicting surgical outcomes. This study aimed to construct a BSCM surgery outcome prediction model based on clinical characteristics and T2-weighted MRI-based radiomics. Two separate cohorts of patients undergoing BSCM resection were included as discovery and validation sets. Patient characteristics and imaging data were analyzed. An unfavorable outcome was defined as a modified Rankin Scale score > 2 at the 12-month follow-up. Image features were extracted from regions of interest within lesions and adjacent brainstem. A nomogram was constructed using the risk score from the optimal model. The discovery and validation sets comprised 218 and 49 patients, respectively (mean age 40 ± 14 years, 127 females); 63 patients in the discovery set and 35 in the validation set had an unfavorable outcome. The eXtreme Gradient Boosting imaging model with selected radiomics features achieved the best performance (area under the receiver operating characteristic curve [AUC] 0.82). Patients were stratified into high- and low-risk groups based on risk scores computed from this model (optimal cutoff 0.37). The final integrative multimodal prognostic model attained an AUC of 0.90, surpassing both the imaging and clinical models alone. Inclusion of BSCM and brainstem subregion imaging data in machine learning models yielded significant predictive capability for unfavorable postoperative outcomes. The integration of specific clinical features enhanced prediction accuracy.

Mechanical complications are a vexing occurrence after adult spinal deformity (ASD) surgery. While achieving ideal spinal alignment in ASD surgery is critical, alignment alone may not fully explain all mechanical complications. The authors sought to determine which combination of inputs produced the most sensitive and specific machine learning model to predict mechanical complications using postoperative alignment, bone quality, and soft tissue data. A retrospective cohort study was performed in patients undergoing ASD surgery from 2009 to 2021. Inclusion criteria were a fusion ≥ 5 levels, sagittal/coronal deformity, and at least 2 years of follow-up. The primary exposure variables were 1) alignment, evaluated in both the sagittal and coronal planes using the L1-pelvic angle ± 3°, L4-S1 lordosis, sagittal vertical axis, pelvic tilt, and coronal vertical axis; 2) bone quality, evaluated by the T-score from a dual-energy x-ray absorptiometry scan; and 3) soft tissue, evaluated by the paraspinal muscle-to-vertebral body ratio and fatty infiltration. The primary outcome was mechanical complications. Alongside demographic data in each model, 7 machine learning models with all combinations of domains (alignment, bone quality, and soft tissue) were trained. The positive predictive value (PPV) was calculated for each model. Of 231 patients (24% male) undergoing ASD surgery with a mean age of 64 ± 17 years, 147 (64%) developed at least one mechanical complication. The model with alignment alone performed poorly, with a PPV of 0.85. However, the model with alignment, bone quality, and soft tissue achieved a high PPV of 0.90, sensitivity of 0.67, and specificity of 0.84. Moreover, the model with alignment alone failed to predict 15 complications of 100, whereas the model with all three domains only failed to predict 10 of 100. These results support the notion that not every mechanical failure is explained by alignment alone. The authors found that a combination of alignment, bone quality, and soft tissue provided the most accurate prediction of mechanical complications after ASD surgery. While achieving optimal alignment is essential, additional data including bone and soft tissue are necessary to minimize mechanical complications.

To develop and evaluate orbital CT deep learning (DL) models in optic neuropathy (ON) prediction in patients diagnosed with thyroid eye disease (TED), using partial versus entire 2D versus 3D images for input. Patients with TED ±ON diagnosed at a quaternary-level practice and who underwent orbital CT between 2002 and 2017 were included. DL models were developed using annotated CT data. The DL models were used to evaluate the hold-out test set. ON classification performances were compared between models and medical specialists, and saliency maps applied to randomized cases. 36/252 orbits in 126 TED patients (mean age, 51 years; 81 women) had clinically confirmed ON. With 2D image input for ON prediction, our models achieved (a) sensitivity 89%, AUC 0.86 on entire coronal orbital apex including bony walls, and (b) specificity 92%, AUC 0.79 on partial axial lateral orbital wall only annotations. ON classification performance was similar (<i>p</i> = 0.58) between DL model and medical specialists. DL models trained on 2D CT annotations rival medical specialists in ON classification, with potential to objectively enhance clinical triage for sight-saving intervention and incorporate model variants in the workflow to harness differential performance metrics.

To measure the association between antithrombotic (AT) medications (anticoagulant and antiplatelet) and risk for traumatic intracranial hemorrhage (ICH) in older adults with a mild traumatic brain injury (mTBI). We conducted a retrospective multicenter study across 103 emergency departments affiliated with a teleradiology company dedicated to emergency imaging between 2020 and 2022. Older adults (≥65 years old) with mTBI, with a head computed tomography scan, were included. Natural language processing models were used to label-free texts of emergency physician forms and radiology reports; and a multivariable logistic regression model to measure the association between AT medications and occurrence of ICH. A total of 5948 patients [median age 84.6 (74.3-89.1) years, 58.1% females] were included, of whom 781 (13.1%) had an ICH. Among them, 3177 (53.4%) patients were treated with at least one AT agent. No AT medication was associated with a higher risk for ICH: antiplatelet odds ratio 0.98 95% confidence interval (0.81-1.18), direct oral anticoagulant 0.82 (0.60-1.09), and vitamin K antagonist 0.66 (0.37-1.10). Conversely, a high-level fall [1.68 (1.15-2.4)], a Glasgow coma scale of 14 [1.83 (1.22-2.68)], a cutaneous head impact [1.5 (1.17-1.92)], vomiting [1.59 (1.18-2.14)], amnesia [1.35 (1.02-1.79)], a suspected skull vault fracture [9.3 (14.2-26.5)] or of facial bones fracture [1.34 (1.02-1.75)] were associated with a higher risk for ICH. This study found no association between AT medications and an increased risk of ICH among older patients with mTBI suggesting that routine neuroimaging in this population may offer limited benefit and that additional variables should be considered in the imaging decision.