This study constructed an interpretable machine learning model based on multi-parameter MRI sub-region habitat radiomics and clinicopathological features, aiming to preoperatively evaluate the microsatellite instability (MSI) status of rectal cancer (RC) patients. This retrospective study recruited 291 rectal cancer patients with pathologically confirmed MSI status and randomly divided them into a training cohort and a testing cohort at a ratio of 8:2. First, the K-means method was used for cluster analysis of tumor voxels, and sub-region radiomics features and classical radiomics features were respectively extracted from multi-parameter MRI sequences. Then, the synthetic minority over-sampling technique method was used to balance the sample size, and finally, the features were screened. Prediction models were established using logistic regression based on clinicopathological variables, classical radiomics features, and MSI-related sub-region radiomics features, and the contribution of each feature to the model decision was quantified by the Shapley-Additive-Explanations (SHAP) algorithm. The area under the curve (AUC) of the sub-region radiomics model in the training and testing groups was 0.848 and 0.8, respectively, both better than that of the classical radiomics and clinical models. The combined model performed the best, with AUCs of 0.908 and 0.863 in the training and testing groups, respectively. We developed and validated a robust combined model that integrates clinical variables, classical radiomics features, and sub-region radiomics features to accurately determine the MSI status of RC patients. We visualized the prediction process using SHAP, enabling more effective personalized treatment plans and ultimately improving RC patient survival rates.

This study aims to develop and validate an ultrasoundbased habitat imaging and peritumoral radiomics model for predicting high-risk capsule characteristics for recurrence of pleomorphic adenoma (PA) of the parotid gland while also exploring the optimal range of peritumoral region. Retrospective analysis was conducted on 325 patients (171 in training set, 74 in validation set and 80 in testing set) diagnosed with PA at two medical centers. Univariate and multivariate logistic regression analyses were performed to identify clinical risk factors. The tumor was segmented into four habitat subregions using K-means clustering, with peri-tumor regions expanded at thicknesses of 1/3/5mm. Radiomics features were extracted from intra-tumor, habitat subregions, and peritumoral regions respectively to construct predictive models, integrating three machine learning classifiers: SVM, RandomForest, and XGBoost. Additionally, a combined model was developed by incorporating peritumoral features and clinical factors based on habitat imaging. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). SHAP analysis was employed to improve the interpretability. The RandomForest model in habitat imaging consistently outperformed other models in predictive performance, with AUC values of 0.881, 0.823, and 0.823 for the training set, validation set, and testing set respectively. Incorporating peri-1mm features and clinical factors into the combined model slightly improved its performance, resulting in AUC values of 0.898, 0.833, and 0.829 for each set. The calibration curves and DCA exhibited excellent fit for the combined model while providing great clinical net benefit. The combined model exhibits robust predictive performance in identifying high-risk capsule characteristics for recurrence of PA in the parotid gland. This model may assist in determining optimal surgical margin and assessing patients' prognosis.

To investigate the effect of accelerated deep-learning (DL) multi-b-value DWI (Mb-DWI) on acquisition time, image quality, and predictive ability of microvascular invasion (MVI) in BCLC stage A hepatocellular carcinoma (HCC), compared to standard Mb-DWI. Patients who underwent liver MRI were prospectively collected. Subjective image quality, signal-to-noise ratio (SNR), lesion contrast-to-noise ratio (CNR), and Mb-DWI-derived parameters from various models (mono-exponential model, intravoxel incoherent motion, diffusion kurtosis imaging, and stretched exponential model) were calculated and compared between the two sequences. The Mb-DWI parameters of two sequences were compared between MVI-positive and MVI-negative groups, respectively. ROC and logistic regression analysis were performed to evaluate and identify the predictive performance. The study included 118 patients. 48/118 (40.67%) lesions were identified as MVI positive. DL Mb-DWI significantly reduced acquisition time by 52.86%. DL Mb-DWI produced significantly higher overall image quality, SNR, and CNR than standard Mb-DWI. All diffusion-related parameters except pseudo-diffusion coefficient showed significant differences between the two sequences. Both in DL and standard Mb-DWI, the apparent diffusion coefficient, true diffusion coefficient (D), perfusion fraction (f), mean diffusivity (MD), mean kurtosis (MK), and distributed diffusion coefficient (DDC) values were significantly different between MVI-positive and MVI-negative groups. The combination of D, f, and MK yield the highest AUC of 0.912 and 0.928 in standard and DL sequences, with no significant difference regarding the predictive efficiency. The DL Mb-DWI significantly reduces acquisition time and improves image quality, with comparable predictive performance to standard Mb-DWI in discriminating MVI status in BCLC stage A HCC.

Achondroplasia, the most common chondrodysplasia in humans, is caused by one of two gain of function mutations localized in the transmembrane domain of fibroblast growth factor receptor 3 (FGFR3) leading to constitutive activation of FGFR3 and subsequent growth plate cartilage and bone defects. Phenotypic features of achondroplasia include macrocephaly with frontal bossing, midface hypoplasia, disproportionate shortening of the extremities, brachydactyly with trident configuration of the hand, and bowed legs. The condition is defined primarily on postnatal effects on bone and cartilage, and embryonic development of tissues in affected individuals is not well studied. Using the Fgfr3^Y367C/+ mouse model of achondroplasia, we investigated the developing chondrocranium and Meckel's cartilage (MC) at embryonic days (E)14.5 and E16.5. Sparse hand annotations of chondrocranial and MC cartilages visualized in phosphotungstic acid enhanced three-dimensional (3D) micro-computed tomography (microCT) images were used to train our automatic deep learning-based 3D segmentation model and produce 3D isosurfaces of the chondrocranium and MC. Using 3D coordinates of landmarks measured on the 3D isosurfaces, we quantified differences in the chondrocranium and MC of Fgfr3^Y367C/+ mice relative to those of their unaffected littermates. Statistically significant differences in morphology and growth of the chondrocranium and MC were found, indicating direct effects of this Fgfr3 mutation on embryonic cranial and pharyngeal cartilages, which in turn can secondarily affect cranial dermal bone development. Our results support the suggestion that early therapeutic intervention during cartilage formation may lessen the effects of this condition.

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.

To develop and validate a machine learning-based prediction model for the use of multiparametric magnetic resonance imaging(MRI) to predict benign and malignant lesions in the testis. The study retrospectively enrolled 148 patients with pathologically confirmed benign and malignant testicular lesions, dividing them into: training set (n=103) and validation set (n=45). Radiomics characteristics were derived from T2-weighted(T2WI)、contrast-enhanced T1-weighted(CE-T1WI)、diffusion-weighted imaging(DWI) and Apparent diffusion coefficient(ADC) MRI images, followed by feature selection. A machine learning-based combined model was developed by incorporating radiomics scores (rad scores) from the optimal radiomics model along with clinical predictors. Draw the receiver operating characteristic (ROC) curve and use the area under the curve (AUC) to evaluate and compare the predictive performance of each model. The diagnostic efficacy of the various machine learning models was evaluated using the Delong test. Radiomics features were extracted from four sequence-based groups(CE-T1WI+DWI+ADC+T2WI), and the model that combined Logistic Regression(LR) machine learning showed the best performance in the radiomics model. The clinical model identified one independent predictors. The combined clinical-radiomics model showed the best performance, whose AUC value was 0.932(95% confidence intervals(CI)0.868-0.978), sensitivity was 0.875, specificity was 0.871 and accuracy was 0.884 in validation set. The combined clinical-radiomics model can be used as a reliable tool to predict benign and malignant testicular lesions and provide a reference for clinical treatment method decisions.

To determine the accuracy of automatic Cobb angle measurements by deep learning (DL) on full spine radiographs. Full spine radiographs of patients aged > 2 years were screened using the radiology reports to identify radiographs for performing Cobb angle measurements. Two senior musculoskeletal radiologists and one senior orthopedic surgeon independently annotated Cobb angles exceeding 7° indicating the angle location as either proximal thoracic (apices between T3 and T5), main thoracic (apices between T6 and T11), or thoraco-lumbar (apices between T12 and L4). If at least two readers agreed on the number of angles, location of the angles, and difference between comparable angles was < 8°, then the ground truth was defined as the mean of their measurements. Otherwise, the radiographs were reviewed by the three annotators in consensus. The DL software (BoneMetrics, Gleamer) was evaluated against the manual annotation in terms of mean absolute error (MAE). A total of 345 patients were included in the study (age 33 ± 24 years, 221 women): 179 pediatric patients (< 22 years old) and 166 adult patients (22 to 85 years old). Fifty-three cases were reviewed in consensus. The MAE of the DL algorithm for the main curvature was 2.6° (95% CI [2.0; 3.3]). For the subgroup of pediatric patients, the MAE was 1.9° (95% CI [1.6; 2.2]) versus 3.3° (95% CI [2.2; 4.8]) for adults. The DL algorithm predicted the Cobb angle of scoliotic patients with high accuracy.

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.

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.

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.