To evaluate the value of combining American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) with the Demetics ultrasound diagnostic system in reducing the rate of fine-needle aspiration (FNA) biopsies for thyroid nodules. A retrospective study analyzed 548 thyroid nodules from 454 patients, all meeting ACR TI-RADS guidelines (category ≥3 and diameter ≥10 mm) for FNA. Nodule was reclassified using the combined ACR TI-RADS and Demetics system (De TI-RADS), and the biopsy rates were compared. Using ACR TI-RADS alone, the biopsy rate was 70.6% (387/548), with a positive predictive value (PPV) of 52.5% (203/387), an unnecessary biopsy rate of 47.5% (184/387) and a missed diagnosis rate of 11.0% (25/228). Incorporating Demetics reduced the biopsy rate to 48.1% (264/548), the unnecessary biopsy rate to 17.4% (46/265) and the missed diagnosis rate to 4.4% (10/228), while increasing PPV to 82.6% (218/264). All differences between ACR TI-RADS and De TI-RADS were statistically significant (p < 0.05). The integration of ACR TI-RADS with the Demetics system improves nodule risk assessment by enhancing diagnostic and efficiency. This approach reduces unnecessary biopsies and missed diagnoses while increasing PPV, offering a more reliable tool for clinicians and patients.

This study aimed to evaluate the diagnostic performance of an artificial intelligence (AI)-based platform (Diagnocat) in detecting periapical radiolucencies (PARLs) in cone-beam computed tomography (CBCT) scans of molars. Specifically, we assessed Diagnocat's performance in detecting PARLs in non-root-filled molars and compared its diagnostic performance between preoperative and postoperative scans. This retrospective study analyzed preoperative and postoperative CBCT scans of 134 molars (327 roots). PARLs detected by Diagnocat were compared with assessments independently performed by two experienced endodontists, serving as the reference standard. Diagnostic performance was assessed at both tooth and root levels using sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), F1 score, and the area under the receiver operating characteristic curve (AUC-ROC). In preoperative scans of non-root-filled molars, Diagnocat demonstrated high sensitivity (teeth: 93.9%, roots: 86.2%), moderate specificity (teeth: 65.2%, roots: 79.9%), accuracy (teeth: 79.1%, roots: 82.6%), PPV (teeth: 71.8%, roots: 75.8%), NPV (teeth: 91.8%, roots: 88.8%), and F1 score (teeth: 81.3%, roots: 80.7%) for PARL detection. The AUC was 0.76 at the tooth level and 0.79 at the root level. Postoperative scans showed significantly lower PPV (teeth: 54.2%; roots: 46.9%) and F1 scores (teeth: 67.2%; roots: 59.2%). Diagnocat shows promise in detecting PARLs in CBCT scans of non-root-filled molars, demonstrating high sensitivity but moderate specificity, highlighting the need for human oversight to prevent overdiagnosis. However, diagnostic performance declined significantly in postoperative scans of root-filled molars. Further research is needed to optimize the platform's performance and support its integration into clinical practice. AI-based platforms such as Diagnocat can assist clinicians in detecting PARLs in CBCT scans, enhancing diagnostic efficiency and supporting decision-making. However, human expertise remains essential to minimize the risk of overdiagnosis and avoid unnecessary treatment.

Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of cancer-related deaths worldwide, with a 5-year survival rate below 20 %. Immunotherapy, particularly immune checkpoint blockade (ICB)-based therapies, has become an important approach for CRC treatment. However, only specific patient subsets demonstrate significant clinical benefits. Although the TIDE algorithm can predict immunotherapy responses, the reliance on transcriptome sequencing data limits its clinical applicability. Recent advances in artificial intelligence and computational pathology provide new avenues for medical image analysis.In this study, we classified TCGA-CRC samples into immunotherapy responder and non-responder groups using the TIDE algorithm. Further, a pathomics model based on convolutional neural networks was constructed to directly predict immunotherapy responses from histopathological images. Single-cell analysis revealed that fibroblasts may induce immunotherapy resistance in CRC through collagen-CD44 and ITGA1 + ITGB1 signaling axes. The developed pathomics model demonstrated excellent classification performance in the test set, with an AUC of 0.88 at the patch level and 0.85 at the patient level. Moreover, key pathomics features were identified through SHAP analysis. This innovative predictive tool provides a novel method for clinical decision-making in CRC immunotherapy, with potential to optimize treatment strategies and advance precision medicine.

Accurate computer-aided diagnosis systems rely on precise segmentation of the vertebral column to assist physicians in diagnosing various disorders. However, segmenting spinal disks and bones becomes challenging in the presence of abnormalities and complex anatomical structures. While Deep Convolutional Neural Networks (DCNNs) achieve remarkable results in medical image segmentation, their performance is limited by data insufficiency and the high computational complexity of existing solutions. This paper introduces LiDSCUNet++, a lightweight deep learning framework based on depthwise-separable and pointwise convolutions integrated with UNet++ for vertebral column segmentation. The model segments vertebral anomalies from dog radiographs, and the results are further processed by YOLOv8 for automated detection of Spondylosis Deformans. LiDSCUNet++ delivers comparable segmentation performance while significantly reducing trainable parameters, memory usage, energy consumption, and computational time, making it an efficient and practical solution for medical image analysis.

Aortic pulse wave velocity (PWV) is a prognostic biomarker for cardiovascular disease, which can be measured by dividing the aortic path length by the pulse transit time. However, current MRI techniques require special sequences and time-consuming manual analysis. We aimed to fully automate the process using deep learning to measure PWV from standard sequences, facilitating PWV measurement in routine clinical and research scans. A deep learning (DL) model was developed to generate high-resolution 3D aortic segmentations from routine 2D trans-axial SSFP localizer images, and the centerlines of the resulting segmentations were used to estimate the aortic path length. A further DL model was built to automatically segment the ascending and descending aorta in phase contrast images, and pulse transit time was estimated from the sampled flow curves. Quantitative comparison with trained observers was performed for path length, aortic flow segmentation and transit time, either using an external clinical dataset with both localizers and paired 3D images acquired or on a sample of UK Biobank subjects. Potential application to clinical research scans was evaluated on 1053 subjects from the UK Biobank. Aortic path length measurement was accurate with no major difference between the proposed method (125 ± 19 mm) and manual measurement by a trained observer (124 ± 19 mm) (P = 0.88). Automated phase contrast image segmentation was similar to that of a trained observer for both the ascending (Dice vs manual: 0.96) and descending (Dice 0.89) aorta with no major difference in transit time estimation (proposed method = 21 ± 9 ms, manual = 22 ± 9 ms; P = 0.15). 966 of 1053 (92 %) UK Biobank subjects were successfully analyzed, with a median PWV of 6.8 m/s, increasing 27 % per decade of age and 6.5 % higher per 10 mmHg higher systolic blood pressure. We describe a fully automated method for measuring PWV from standard cardiac MRI localizers and a single phase contrast imaging plane. The method is robust and can be applied to routine clinical scans, and could unlock the potential of measuring PWV in large-scale clinical and population studies. All models and deployment codes are available online.

Pulmonary hypertension (pH) is a malignant pulmonary circulation disease. Right heart catheterization (RHC) is the gold standard procedure for quantitative evaluation of pulmonary hemodynamics. Accurate and noninvasive quantitative evaluation of pulmonary hemodynamics is challenging due to the limitations of currently available assessment methods. Patients who underwent computed tomographic pulmonary angiography (CTPA) and RHC examinations within 2 weeks were included. The dataset was randomly divided into a training set and a test set at an 8:2 ratio. A radiomic feature model and another two-dimensional (2D) feature model aimed to quantitatively evaluate of pulmonary hemodynamics were constructed. The performance of models was determined by calculating the mean squared error, the intraclass correlation coefficient (ICC) and the area under the precision-recall curve (AUC-PR) and performing Bland-Altman analyses. 345 patients: 271 patients with PH (mean age 50 ± 17 years, 93 men) and 74 without PH (mean age 55 ± 16 years, 26 men) were identified. The predictive results of pulmonary hemodynamics of radiomic feature model integrating 5 2D features and other 30 radiomic features were consistent with the results from RHC, and outperformed another 2D feature model. The radiomic feature model exhibited moderate to good reproducibility to predict pulmonary hemodynamic parameters (ICC reached 0.87). In addition, pH can be accurately identified based on a classification model (AUC-PR =0.99). This study provides a noninvasive method for comprehensively and quantitatively evaluating pulmonary hemodynamics using CTPA images, which has the potential to serve as an alternative to RHC, pending further validation.

Immune checkpoint inhibitors (ICIs) and targeted therapies have revolutionized the management of metastatic renal cell carcinoma (mRCC). Currently, the frontline standard of care for patients with mRCC involves the provision of systemic ICI-based combination therapy with no clear guidelines on holding or de-escalating treatment, even with a complete or partial radiological response. Treatments usually continue until disease progression or unacceptable toxicity, frequently leading to overtreatment, which can elevate the risk of toxicity without providing a corresponding increase in therapeutic efficacy. In addition, the ongoing use of expensive antineoplastic drugs increases the financial burden on the already overstretched health care systems and on patients and their families. De-escalation strategies could be designed by integrating contemporary technologies, such as circulating tumor DNA, and advanced imaging techniques, such as computed tomography (CT) scans, positron emission tomography CT, magnetic resonance imaging, and machine learning models. Treatment de-escalation, when appropriate, can minimize treatment-related toxicities, reduce health care costs, and optimize the patients' quality of life while maintaining effective cancer control. This paper discusses the advantages, challenges, and clinical implications of de-escalation strategies in the management of mRCC. PATIENT SUMMARY: In this report, we describe the burden of overtreatment in patients who are never able to stop treatments for metastatic kidney cancer. We discuss the application of the latest technology that can help in making de-escalation decisions.

This study seeks to develop a combined model integrating clinical data, radiomics, and deep learning (DL) for predicting the efficacy of posterior lumbar interbody fusion (PLIF) surgery. A retrospective review was conducted on 461 patients who underwent PLIF for degenerative lumbar diseases. These patients were partitioned into a training set (n=368) and a test set (n=93) in an 8:2 ratio. Clinical models, radiomics models, and DL models were constructed based on logistic regression and random forest, respectively. A combined model was established by integrating these three models. All radiomics and DL features were extracted from sagittal T2-weighted images using 3D slicer software. The least absolute shrinkage and selection operator method selected the optimal radiomics and DL features to build the models. In addition to analyzing the original region of interest (ROI), we also conducted different degrees of mask expansion on the ROI to determine the optimal ROI. The performance of the model was evaluated by using the receiver operating characteristic curve (ROC) and the area under the ROC curve. The differences in AUC were compared by DeLong test. Among the clinical characteristics, patient age, body weight, and preoperative intervertebral distance at the surgical segment are risk factors affecting the fusion outcome. The radiomics model based on MRI with expanded 10 mm mask showed excellent performance (training set AUC=0.814, 95% CI: (0.761-0.866); test set AUC=0.749, 95% CI: [0.631-0.866]). Among all single models, the DL model had the best diagnostic prediction performance, with AUC values of (0.995, 95% CI: [0.991-0.999]) for the training set and (0.803, 95% CI: [0.705-0.902]) for the test set. Compared to all the models, the combined model of clinical features, radiomics features, and DL features had the best diagnostic prediction performance, with AUC values of (0.993, 95% CI: [0.987-0.999]) for the training set and (0.866, 95% CI: [0.778-0.955]) for the test set. The proposed clinical feature-deep learning radiomics model can effectively predict the postoperative efficacy of patients undergoing PLIF surgery and has good clinical applicability.

This study develops a deep-learning method for automatic segmentation of the interventricular septum (IS) in MR images to measure myocardial T2* and estimate cardiac iron deposition in patients with thalassemia. This retrospective study used multiple-gradient-echo cardiac MR scans from 419 thalassemia patients to develop and evaluate the segmentation network. The network was trained on 1.5 T images from Center 1 and evaluated on 3.0 T unseen images from Center 1, all data from Center 2, and the CHMMOTv1 dataset. Model performance was assessed using five metrics, and T2* values were obtained by fitting the network output. Bland-Altman analysis, coefficient of variation (CoV), and regression analysis were used to evaluate the consistency between automatic and manual methods. MA-BBIsegNet achieved a Dice of 0.90 on the internal test set, 0.85 on the external test set, and 0.81 on the CHMMOTv1 dataset. Bland-Altman analysis showed mean differences of 0.08 (95% LoA: -2.79 ∼ 2.63) ms (internal), 0.29 (95% LoA: -4.12 ∼ 3.54) ms (external) and 0.19 (95% LoA: -3.50 ∼ 3.88) ms (CHMMOTv1), with CoV of 8.9%, 6.8%, and 9.3%. Regression analysis yielded r values of 0.98 for the internal and CHMMOTv1 datasets, and 0.99 for the external dataset (p < 0.05). The IS segmentation network based on multiple-gradient-echo bright-blood images yielded T2* values that were in strong agreement with manual measurements, highlighting its potential for the efficient, non-invasive monitoring of myocardial iron deposition in patients with thalassemia.

To compare the image quality and pulmonary nodule detectability and measurement accuracy between deep learning reconstruction (DLR) and hybrid iterative reconstruction (HIR) of chest ultra-low-dose CT (ULDCT). Participants who underwent chest standard-dose CT (SDCT) followed by ULDCT from October 2020 to January 2022 were prospectively included. ULDCT images reconstructed with HIR and DLR were compared with SDCT images to evaluate image quality, nodule detection rate, and measurement accuracy using a commercially available deep learning-based nodule evaluation system. Wilcoxon signed-rank test was used to evaluate the percentage errors of nodule size and nodule volume between HIR and DLR images. Eighty-four participants (54 ± 13 years; 26 men) were finally enrolled. The effective radiation doses of ULDCT and SDCT were 0.16 ± 0.02 mSv and 1.77 ± 0.67 mSv, respectively (P < 0.001). The mean ± standard deviation of the lung tissue noises was 61.4 ± 3.0 HU for SDCT, 61.5 ± 2.8 HU and 55.1 ± 3.4 HU for ULDCT reconstructed with HIR-Strong setting (HIR-Str) and DLR-Strong setting (DLR-Str), respectively (P < 0.001). A total of 535 nodules were detected. The nodule detection rates of ULDCT HIR-Str and ULDCT DLR-Str were 74.0% and 83.4%, respectively (P < 0.001). The absolute percentage error in nodule volume from that of SDCT was 19.5% in ULDCT HIR-Str versus 17.9% in ULDCT DLR-Str (P < 0.001). Compared with HIR, DLR reduced image noise, increased nodule detection rate, and improved measurement accuracy of nodule volume at chest ULDCT. Not applicable.