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 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.

Dens evaginatus is a dental morphological developmental anomaly. Failing to detect it may lead to tubercles fracture and pulpal/periapical disease. Consequently, early detection and intervention of dens evaginatus are significant to preserve vital pulp. This study aimed to develop a deep learning model to assist dentists in early diagnosing dens evaginatus, thereby supporting early intervention and mitigating the risk of severe consequences. In this study, a deep learning model was developed utilizing panoramic radiograph images sourced from 1410 patients aged 3-16 years, with high-quality annotations to enable the automatic detection of dens evaginatus. Model performance and model's efficacy in aiding dentists were evaluated. The findings indicated that the current deep learning model demonstrated commendable sensitivity (0.8600) and specificity (0.9200), outperforming dentists in detecting dens evaginatus with an F1-score of 0.8866 compared to their average F1-score of 0.8780, indicating that the model could detect dens evaginatus with greater precision. Furthermore, with its support, young dentists heightened their focus on dens evaginatus in tooth germs and achieved improved diagnostic accuracy. Based on these results, the integration of deep learning for dens evaginatus detection holds significance and can augment dentists' proficiency in identifying such anomaly.

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.

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.

Accurate vertebral segmentation is an important step in imaging analysis pipelines for diagnosis and subsequent treatment of spinal metastases. Segmenting these metastases is especially challenging given their radiological heterogeneity. Conventional approaches for segmenting vertebrae have included manual review or deep learning; however, manual review is time-intensive with interrater reliability issues, while deep learning requires large datasets to build. The rise of generative AI, notably tools such as Meta's Segment Anything Model 2 (SAM 2), holds promise in its ability to rapidly generate segmentations of any image without pretraining (zero-shot). The authors of this study aimed to assess the ability of SAM 2 to segment vertebrae with metastases. A publicly available set of spinal CT scans from The Cancer Imaging Archive was used, which included patient sex, BMI, vertebral locations, types of metastatic lesion (lytic, blastic, or mixed), and primary cancer type. Ground-truth segmentations for each vertebra, derived by neuroradiologists, were further extracted from the dataset. SAM 2 then produced segmentations for each vertebral slice without any training data, all of which were compared to gold standard segmentations using the Dice similarity coefficient (DSC). Relative performance differences were assessed across clinical subgroups using standard statistical techniques. Imaging data were extracted for 55 patients and 779 unique thoracolumbar vertebrae, 167 of which had metastatic tumor involvement. Across these vertebrae, SAM 2 had a mean volumetric DSC of 0.833 ± 0.053. SAM 2 performed significantly worse on thoracic vertebrae relative to lumbar vertebrae, female patients relative to male patients, and obese patients relative to non-obese patients. These results demonstrate that general-purpose segmentation models like SAM 2 can provide reasonable vertebral segmentation accuracy with no pretraining, with efficacy comparable to previously published trained models. Future research should include optimizations of spine segmentation models for vertebral location and patient body habitus, as well as for variations in imaging quality approaches.

The aim of this study was to develop and validate a fully automatic anatomical landmark localization and trajectory planning method for external ventricular drain (EVD) placement using CT or MRI. The authors used 125 preoperative CT and 137 contrast-enhanced T1-weighted MRI scans to generate 3D surface meshes of patients' skin and ventricular systems. Seven anatomical landmarks were manually annotated to train a neural network for automatic landmark localization. The model's accuracy was assessed by calculating the mean Euclidian distance of predicted landmarks to the ground truth. Kocher's point and EVD trajectories were automatically calculated with the foramen of Monro as the target. Performance was evaluated using Kakarla grades, as assessed by 3 clinicians. Interobserver agreement was measured with Pearson correlation, and scores were aggregated using majority voting. Ordinal linear regressions were used to assess whether modality or placement side had an effect on Kakarla grades. The impact of landmark localization error on the final EVD plan was also evaluated. The automated landmark localization model achieved a mean error of 4.0 mm (SD 2.6 mm). Trajectory planning generated a trajectory for all patients, with a Kakarla grade of 1 in 92.9% of cases. Statistical analyses indicated a strong interobserver agreement and no significant differences between modalities (CT vs MRI) or EVD placement sides. The location of Kocher's point and the target point were significantly correlated to nasion landmark localization error, with median drifts of 9.38 mm (95% CI 1.94-19.16 mm) and 3.91 mm (95% CI 0.18-26.76 mm) for Kocher's point and the target point, respectively. The presented method was efficient and robust for landmark localization and accurate EVD trajectory planning. The short processing time thereby also provides a base for use in emergency settings.

Due to the complex anatomical structure and dynamic involution process of the thymus, segmentation and evaluation of the thymus in medical imaging present significant challenges. The aim of this study is to develop a deep-learning tool "Thy-uNET" for automatic segmentation and measurement of the thymus or thymic region on chest CT imaging, and to validate its performance with multicenter data. Utilizing the segmentation and measurement results from two experts, training of Thy-uNET was conducted on training cohort (n = 500). The segmented regions include thymus or thymic region, and 7 features of the thymic region were measured. The automatic segmentation performance was assessed using Dice and Intersection over Union (IOU) on CT data from three test cohorts (n = 286). Spearman correlation analysis and intraclass correlation coefficient (ICC) were used to evaluate the correlation and reliability of the automatic measurement results. Six radiologists with varying levels of experience were invited to participate in a reader study to assess the measurement performance of Thy-uNET and its ability to assist doctors. Thy-uNET demonstrated consistent segmentation performance across different subgroups, with Dice = 0.83 in the internal test set, and Dice = 0.82 in the external test sets. For automatic measurement of thymic features, Thy-uNET achieved high correlation coefficients and ICC for key measurements (R = 0.829 and ICC = 0.841 for CT attenuation measurement). Its performance was comparable to that of radiology residents and junior radiologists, with significantly shorter measurement time. Providing Thy-uNET measurements to readers reduced their measurement time and improved residents' performance in some thymic feature measurements. Thy-uNET can provide reliable automatic segmentation and automatic measurement information of the thymus or thymic region on routine CT, reducing time costs and improving the consistency of evaluations.

Accurate and robust segmentation of anatomical structures in brain MRI provides a crucial basis for the subsequent observation, analysis, and treatment planning of various brain diseases. Deep learning foundation models trained and designed on large-scale natural scene image datasets experience significant performance degradation when applied to subcortical brain structure segmentation in MRI, limiting their direct applicability in clinical diagnosis. This paper proposes a subcortical brain structure segmentation algorithm based on Low-Rank Adaptation (LoRA) to fine-tune SAM (Segment Anything Model) by freezing SAM's image encoder and applying LoRA to approximate low-rank matrix updates to the encoder's training weights, while also fine-tuning SAM's lightweight prompt encoder and mask decoder. The fine-tuned model's learnable parameters (5.92 MB) occupy only 6.39% of the original model's parameter size (92.61 MB). For training, model preheating is employed to stabilize the fine-tuning process. During inference, adaptive prompt learning with point or box prompts is introduced to enhance the model's accuracy for arbitrary brain MRI segmentation. This interactive prompt learning approach provides clinicians with a means of intelligent segmentation for deep brain structures, effectively addressing the challenges of limited data labels and high manual annotation costs in medical image segmentation. We use five MRI datasets of IBSR, MALC, LONI, LPBA, Hammers and CANDI for experiments across various segmentation scenarios, including cross-domain settings with inference samples from diverse MRI datasets and supervised fine-tuning settings, demonstrate the proposed segmentation algorithm's generalization and effectiveness when compared to current mainstream and supervised segmentation algorithms.

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.