To validate a liver lesion detection and classification model using staging computed tomography (CT) scans of colorectal cancer (CRC) patients. A UNet-based deep learning model was trained on 272 public liver tumour CT scans and tested on 220 CRC staging CTs acquired from a single institution (2014-2019). Performance metrics included lesion detection rates by size (<10 mm, 10-20 mm, >20 mm), segmentation accuracy (dice similarity coefficient, DSC), volume measurement agreement (Bland-Altman limits of agreement, LOAs; intraclass correlation coefficient, ICC), and classification accuracy (malignant vs benign) at patient and lesion levels (detected lesions only). The model detected 743 out of 884 lesions (84%), with detection rates of 75%, 91.3%, and 96% for lesions <10 mm, 10-20 mm, and >20 mm, respectively. The median DSC was 0.76 (95% CI: 0.72-0.80) for lesions <10 mm, 0.83 (95% CI: 0.79-0.86) for 10-20 mm, and 0.85 (95% CI: 0.82-0.88) for >20 mm. Bland-Altman analysis showed a mean volume bias of -0.12 cm³ (LOAs: -1.68 to +1.43 cm³), and ICC was 0.81. Lesion-level classification showed 99.5% sensitivity, 65.7% specificity, 53.8% positive predictive value (PPV), 99.7% negative predictive value (NPV), and 75.4% accuracy. Patient-level classification had 100% sensitivity, 27.1% specificity, 59.2% PPV, 100% NPV, and 64.5% accuracy. The model demonstrates strong lesion detection and segmentation performance, particularly for sub-centimetre lesions. Although classification accuracy was moderate, the 100% NPV suggests strong potential as a CRC staging screening tool. Future studies will assess its impact on radiologist performance and efficiency.

Renal disorders are a significant public health concern and a cause of mortality related to renal failure. Manual diagnosis is subjective, labor-intensive, and depends on the expertise of nephrologists in renal anatomy. To improve workflow efficiency and enhance diagnosis accuracy, we propose an automated deep learning model, called EACWNet, which incorporates adaptive channel weighting-based deep convolutional neural network and explainable artificial intelligence. The proposed model categorizes renal computed tomography images into various classes, such as cyst, normal, tumor, and stone. The adaptive channel weighting module utilizes both global and local contextual insights to refine the final feature map channel weights through the integration of a scale-adaptive channel attention module in the higher convolutional blocks of the VGG-19 backbone model employed in the proposed method. The efficacy of the EACWNet model has been assessed using a publicly available renal CT images dataset, attaining an accuracy of 98.87% and demonstrating a 1.75% improvement over the backbone model. However, this model exhibits class-wise precision variation, achieving higher precision for cyst, normal, and tumor cases but lower precision for the stone class due to its inherent variability and heterogeneity. Furthermore, the model predictions have been subjected to additional analysis using the explainable artificial intelligence method such as local interpretable model-agnostic explanations, to visualize better and understand the model predictions.

To assess the feasibility of using GPT-4 to simplify radiology reports into B1-level Dutch for enhanced patient comprehension. This study utilised GPT-4, optimised through prompt engineering in Microsoft Azure. The researchers iteratively refined prompts to ensure accurate and comprehensive translations of radiology reports. Two radiologists assessed the simplified outputs for accuracy, completeness, and patient suitability. A third radiologist independently validated the final versions. Twelve colorectal cancer patients were recruited from two hospitals in the Netherlands. Semi-structured interviews were conducted to evaluate patients' comprehension and satisfaction with AI-generated reports. The optimised GPT-4 tool produced simplified reports with high accuracy (mean score 3.33/4). Patient comprehension improved significantly from 2.00 (original reports) to 3.28 (simplified reports) and 3.50 (summaries). Correct classification of report outcomes increased from 63.9% to 83.3%. Patient satisfaction was high (mean 8.30/10), with most preferring the long simplified report. RADiANT successfully enhances patient understanding and satisfaction through automated AI-driven report simplification, offering a scalable solution for patient-centred communication in clinical practice. This tool reduces clinician workload and supports informed patient decision-making, demonstrating the potential of LLMs beyond English-based healthcare contexts.

To evaluate the feasibility of generative pre-trained transformer-4 (GPT-4) in generating structured reports (SRs) from mixed-language (English and Korean) narrative-style CT reports for pancreatic ductal adenocarcinoma (PDAC) and to assess its accuracy in categorizing PDCA resectability. This retrospective study included consecutive free-text reports of pancreas-protocol CT for staging PDAC, from two institutions, written in English or Korean from January 2021 to December 2023. Both the GPT-4 Turbo and GPT-4o models were provided prompts along with the free-text reports via an application programming interface and tasked with generating SRs and categorizing tumor resectability according to the National Comprehensive Cancer Network guidelines version 2.2024. Prompts were optimized using the GPT-4 Turbo model and 50 reports from Institution B. The performances of the GPT-4 Turbo and GPT-4o models in the two tasks were evaluated using 115 reports from Institution A. Results were compared with a reference standard that was manually derived by an abdominal radiologist. Each report was consecutively processed three times, with the most frequent response selected as the final output. Error analysis was guided by the decision rationale provided by the models. Of the 115 narrative reports tested, 96 (83.5%) contained both English and Korean. For SR generation, GPT-4 Turbo and GPT-4o demonstrated comparable accuracies (92.3% [1592/1725] and 92.2% [1590/1725], respectively; <i>P</i> = 0.923). In the resectability categorization, GPT-4 Turbo showed higher accuracy than GPT-4o (81.7% [94/115] vs. 67.0% [77/115], respectively; <i>P</i> = 0.002). In the error analysis of GPT-4 Turbo, the SR generation error rate was 7.7% (133/1725 items), which was primarily attributed to inaccurate data extraction (54.1% [72/133]). The resectability categorization error rate was 18.3% (21/115), with the main cause being violation of the resectability criteria (61.9% [13/21]). Both GPT-4 Turbo and GPT-4o demonstrated acceptable accuracy in generating NCCN-based SRs on PDACs from mixed-language narrative reports. However, oversight by human radiologists is essential for determining resectability based on CT findings.

To develop a semi-supervised domain adaptation technique for metal artifact reduction with a spatial-frequency transformer (SFTrans) model (Semi-SFTrans), and to quantitatively compare its performance with supervised models (Sup-SFTrans and ResUNet) and traditional linear interpolation MAR method (LI) in oral and maxillofacial CT. Supervised models, including Sup-SFTrans and a state-of-the-art model termed ResUNet, were trained with paired simulated CT images, while semi-supervised model, Semi-SFTrans, was trained with both paired simulated and unpaired clinical CT images. For evaluation on the simulated data, we calculated Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) on the images corrected by four methods: LI, ResUNet, Sup-SFTrans, and Semi-SFTrans. For evaluation on the clinical data, we collected twenty-two clinical cases with real metal artifacts, and the corresponding intraoral scan data. Three radiologists visually assessed the severity of artifacts using Likert scales on the original, Sup-SFTrans-corrected, and Semi-SFTrans-corrected images. Quantitative MAR evaluation was conducted by measuring Mean Hounsfield Unit (HU) values, standard deviations, and Signal-to-Noise Ratios (SNRs) across Regions of Interest (ROIs) such as the tongue, bilateral buccal, lips, and bilateral masseter muscles, using paired t-tests and Wilcoxon signed-rank tests. Further, teeth integrity in the corrected images was assessed by comparing teeth segmentation results from the corrected images against the ground-truth segmentation derived from registered intraoral scan data, using Dice Score and Hausdorff Distance. Sup-SFTrans outperformed LI, ResUNet and Semi-SFTrans on the simulated dataset. Visual assessments from the radiologists showed that average scores were (2.02 ± 0.91) for original CT, (4.46 ± 0.51) for Semi-SFTrans CT, and (3.64 ± 0.90) for Sup-SFTrans CT, with intra correlation coefficients (ICCs)＞0.8 of all groups and p < 0.001 between groups. On soft tissue, both Semi-SFTrans and Sup-SFTrans significantly reduced metal artifacts in tongue (p < 0.001), lips, bilateral buccal regions, and masseter muscle areas (p < 0.05). Semi-SFTrans achieved superior metal artifact reduction than Sup-SFTrans in all ROIs (p < 0.001). SNR results indicated significant differences between Semi-SFTrans and Sup-SFTrans in tongue (p = 0.0391), bilateral buccal (p = 0.0067), lips (p = 0.0208), and bilateral masseter muscle areas (p = 0.0031). Notably, Semi-SFTrans demonstrated better teeth integrity preservation than Sup-SFTrans (Dice Score: p < 0.001; Hausdorff Distance: p = 0.0022). The semi-supervised MAR model, Semi-SFTrans, demonstrated superior metal artifact reduction performance over supervised counterparts in real dental CT images.

Esophageal cancer (EC) is one of the leading causes of cancer-related mortality worldwide, still faces significant challenges in early diagnosis and prognosis. Early EC lesions often present subtle symptoms and current diagnostic methods are limited in accuracy due to tumor heterogeneity, lesion morphology and variable image quality. These limitations are particularly prominent in the early detection of precancerous lesions such as Barrett's esophagus. Traditional diagnostic approaches, such as endoscopic examination, pathological analysis and computed tomography, require improvements in diagnostic precision and staging accuracy. Deep learning (DL), a key branch of artificial intelligence, shows great promise in improving the detection of early EC lesions, distinguishing benign from malignant lesions and aiding cancer staging and prognosis. However, challenges remain, including image quality variability, insufficient data annotation and limited generalization. The present review summarized recent advances in the application of DL to medical images obtained through various imaging techniques for the diagnosis of EC at different stages. It assesses the role of DL in tumor pathology, prognosis prediction and clinical decision support, highlighting its advantages in EC diagnosis and prognosis evaluation. Finally, it provided an objective analysis of the challenges currently facing the field and prospects for future applications.

To compare prostate cancer lesion detection using conventional and artificial intelligence (AI)-assisted image interpretation at multiparametric MRI (mpMRI). A retrospective study of 53 consecutive patients who underwent prostate mpMRI and subsequent prostate tissue sampling was performed. Two board-certified radiologists (with 4 and 12 years of experience) blinded to the clinical information interpreted anonymized exams using the PI-RADS v2.1 framework without and with an AI-assistance tool. The AI software tool provided radiologists with gland segmentation and automated lesion detection assigning a probability score for the likelihood of the presence of clinically significant prostate cancer (csPCa). The reference standard for all cases was the prostate pathology from systematic and targeted biopsies. Statistical analyses assessed interrater agreement and compared diagnostic performances with and without AI assistance. Within the entire cohort, 42 patients (79 %) harbored Gleason-positive disease, with 25 patients (47 %) having csPCa. Radiologists' diagnostic performance for csPCa was significantly improved over conventional interpretation with AI assistance (reader A: AUC 0.82 vs. 0.72, p = 0.03; reader B: AUC 0.78 vs. 0.69, p = 0.03). Without AI assistance, 81 % (n = 36; 95 % CI: 0.89-0.91) of the lesions were scored similarly by radiologists for lesion-level characteristics, and with AI assistance, 59 % (26, 0.82-0.89) of the lesions were scored similarly. For reader A, there was a significant difference in PI-RADS scores (p = 0.02) between AI-assisted and non-assisted assessments. Signficant differences were not detected for reader B. AI-assisted prostate mMRI interpretation improved radiologist diagnostic performance over conventional interpretation independent of reader experience.

Obesity is a risk factor for diverticulitis. However, it remains unclear whether visceral fat area, a more precise measurement of abdominal fat, is associated with the risk of diverticulitis. To estimate the risk of incident and recurrent diverticulitis according to visceral fat area. A retrospective cohort study. The Mass General Brigham Biobank. A total of 6654 patients who underwent abdominal CT for clinical indications and had no diagnosis of diverticulitis, IBD, or cancer before the scan were included. Visceral fat area, subcutaneous fat area, and skeletal muscle area were quantified using a deep-learning model applied to abdominal CT. The main exposures were z -scores of body composition metrics normalized by age, sex, and race. Diverticulitis cases were identified using the International Classification of Diseases codes for the primary or admitting diagnosis from the electronic health records. The risks of incident diverticulitis, complicated diverticulitis, and recurrent diverticulitis requiring hospitalization according to quartiles of body composition metrics z -scores were estimated. A higher visceral fat area z -score was associated with an increased risk of incident diverticulitis (multivariable HR comparing the highest vs lowest quartile, 2.09; 95% CI, 1.48-2.95; p for trend <0.0001), complicated diverticulitis (HR, 2.56; 95% CI, 1.10-5.99; p for trend = 0.02), and recurrence requiring hospitalization (HR, 2.76; 95% CI, 1.15-6.62; p for trend = 0.03). The association between visceral fat area and diverticulitis was not materially different among different strata of BMI. Subcutaneous fat area and skeletal muscle area were not significantly associated with diverticulitis. The study population was limited to individuals who underwent CT scans for medical indication. Higher visceral fat area derived from CT was associated with incident and recurrent diverticulitis. Our findings provide insight into the underlying pathophysiology of diverticulitis and may have implications for preventive strategies. See Video Abstract . ANTECEDENTES:La obesidad es un factor de riesgo de la diverticulitis. Sin embargo, sigue sin estar claro si el área de grasa visceral, con medida más precisa de la grasa abdominal esté asociada con el riesgo de diverticulitis.OBJETIVO:Estimar el riesgo de diverticulitis incidente y recurrente de acuerdo con el área de grasa visceral.DISEÑO:Un estudio de cohorte retrospectivo.AJUSTE:El Biobanco Mass General Brigham.PACIENTES:6.654 pacientes sometidos a una TC abdominal por indicaciones clínicas y sin diagnóstico de diverticulitis, enfermedad inflamatoria intestinal o cáncer antes de la exploración.PRINCIPALES MEDIDAS DE RESULTADOS:Se cuantificaron, área de grasa visceral, área de grasa subcutánea y área de músculo esquelético, utilizando un modelo de aprendizaje profundo aplicado a la TC abdominal. Las principales exposiciones fueron puntuaciones z de métricas de composición corporal, normalizadas por edad, sexo y raza. Los casos de diverticulitis se definieron con los códigos ICD para el diagnóstico primario o de admisión de los registros de salud electrónicos. Se estimaron los riesgos de diverticulitis incidente, diverticulitis complicada y diverticulitis recurrente que requiriera hospitalización según los cuartiles de las puntuaciones z de las métricas de composición corporal.RESULTADOS:Una puntuación z más alta del área de grasa visceral se asoció con un mayor riesgo de diverticulitis incidente (HR multivariable que compara el cuartil más alto con el más bajo, 2,09; IC del 95 %, 1,48-2,95; P para la tendencia < 0,0001), diverticulitis complicada (HR, 2,56; IC del 95 %, 1,10-5,99; P para la tendencia = 0,02) y recurrencia que requiriera hospitalización (HR, 2,76; IC del 95 %, 1,15-6,62; P para la tendencia = 0,03). La asociación entre el área de grasa visceral y la diverticulitis no fue materialmente diferente entre los diferentes estratos del índice de masa corporal. El área de grasa subcutánea y el área del músculo esquelético no se asociaron significativamente con la diverticulitis.LIMITACIONES:La población del estudio se limitó a individuos sometidos a tomografías computarizadas por indicación médica.CONCLUSIÓN:Una mayor área de grasa visceral derivada de la tomografía computarizada se asoció con diverticulitis incidente y recurrente. Nuestros hallazgos brindan información sobre la fisiopatología subyacente de la diverticulitis y pueden tener implicaciones para las estrategias preventivas. (Traducción: Dr. Fidel Ruiz Healy ).

To develop and evaluate a deep learning technique for the differentiation of hepatocellular carcinoma (HCC) using "simplified intravoxel incoherent motion (IVIM) parameters" derived from only 3 b-value images. Ninety-eight retrospective magnetic resonance imaging data were collected (68 men, 30 women; mean age 59 ± 14 years), including T2-weighted imaging with fat suppression, in-phase, out-of-phase, and diffusion-weighted imaging (b = 0, 100, 800 s/mm2). Ninety percent of data were used for stratified 10-fold cross-validation. After data preprocessing, diffusion-weighted imaging images were used to compute simplified IVIM and apparent diffusion coefficient (ADC) maps. A 17-layer 3D convolutional neural network (3D-CNN) was implemented, and the input channels were modified for different strategies of input images. The 3D-CNN with IVIM maps (ADC, f, and D*) demonstrated superior performance compared with other strategies, achieving an accuracy of 83.25 ± 6.24% and area under the receiver-operating characteristic curve of 92.70 ± 8.24%, significantly surpassing the baseline of 50% (P < 0.05) and outperforming other strategies in all evaluation metrics. This success underscores the effectiveness of simplified IVIM parameters in combination with a 3D-CNN architecture for enhancing HCC differentiation accuracy. Simplified IVIM parameters derived from 3 b-values, when integrated with a 3D-CNN architecture, offer a robust framework for HCC differentiation.

Preoperative identification of genetic mutations is conducive to individualized treatment and management of papillary thyroid carcinoma (PTC) patients. <i>Purpose</i>: To investigate the predictive value of the machine learning (ML)-based ultrasound (US) radiomics approaches for BRAF V600E and TERT promoter status (individually and coexistence) in PTC. This multicenter study retrospectively collected data of 1076 PTC patients underwent genetic testing detection for BRAF V600E and TERT promoter between March 2016 and December 2021. Radiomics features were extracted from routine grayscale ultrasound images, and gene status-related features were selected. Then these features were included to nine different ML models to predicting different mutations, and optimal models plus statistically significant clinical information were also conducted. The models underwent training and testing, and comparisons were performed. The Decision Tree-based US radiomics approach had superior prediction performance for the BRAF V600E mutation compared to the other eight ML models, with an area under the curve (AUC) of 0.767 versus 0.547-0.675 (p < 0.05). The US radiomics methodology employing Logistic Regression exhibited the highest accuracy in predicting TERT promoter mutations (AUC, 0.802 vs. 0.525-0.701, p < 0.001) and coexisting BRAF V600E and TERT promoter mutations (0.805 vs. 0.678-0.743, p < 0.001) within the test set. The incorporation of clinical factors enhanced predictive performances to 0.810 for BRAF V600E mutant, 0.897 for TERT promoter mutations, and 0.900 for dual mutations in PTCs. The machine learning-based US radiomics methods, integrated with clinical characteristics, demonstrated effectiveness in predicting the BRAF V600E and TERT promoter mutations in PTCs.