Benign lymph node enlargement can mislead surgeons into overstaging colorectal cancer (CRC), causing unnecessarily extended lymphadenectomy. This study aimed to develop and validate a machine learning (ML) classifier utilizing multi-phase CT (MPCT) radiomics for accurate evaluation of the pre-treatment status of enlarged tumor-draining lymph nodes (TDLNs; defined as long-axis diameter ≥ 10 mm). This study included 430 pathologically confirmed CRC patients who underwent radical resection, stratified into a development cohort (n = 319; January 2015-December 2019, retrospectively enrolled) and test cohort (n = 111; January 2020-May 2023, prospectively enrolled). Radiomics features were extracted from multi-regional lesions (tumor and enlarged TDLNs) on MPCT. Following rigorous feature selection, optimal features were employed to train multiple ML classifiers. The top-performing classifier based on area under receiver operating characteristic curves (AUROCs) was validated. Ultimately, 15 classifiers based on features from multi-regional lesions were constructed (Tumor_{N, A}, _V; Ln_N, _A, _V; Ln, lymph node; _N, non-contrast phase; _A, arterial phase; _V, venous phase). Among all classifiers, the enlarged TDLNs fusion MPCT classifier (Ln_NAV) demonstrated the highest predictive efficacy, with AUROCs and AUPRCs of 0.820 and 0.883, respectively. When pre-treatment clinical variables were integrated (Clinical_Ln_NAV), the model's efficacy improved, with AUROCs of 0.839, AUPRCs of 0.903, accuracy of 76.6%, sensitivity of 67.7%, and specificity of 89.1%. The classifier Clinical_Ln_NAV demonstrated well performance in evaluating pre-treatment status of enlarged TDLNs. This tool may support clinicians in developing individualized treatment plans for CRC patients, helping to avoid inappropriate treatment. Question There are currently no effective non-invasive tools to assess the status of enlarged tumor-draining lymph nodes in colorectal cancer prior to treatment. Findings Pre-treatment multi-phase CT radiomics, combined with clinical variables, effectively assessed the status of enlarged tumor-draining lymph nodes, achieving a specificity of 89.1%. Clinical relevance statement The multi-phase CT-based classifier may assist clinicians in developing individualized treatment plans for colorectal cancer patients, potentially helping to avoid inappropriate preoperative adjuvant therapy and unnecessary extended lymphadenectomy.

Dental implant surgery has become a prevalent treatment option for patients with single tooth defects. However, the success of this surgery relies heavily on precise planning and execution. This study investigates the application of artificial intelligence (AI) in assisting the planning process of dental implant surgery for single tooth defects. Single tooth defects in the oral cavity pose a significant challenge in restorative dentistry. Dental implant restoration has emerged as an effective solution for rehabilitating such defects. However, the complexity of the procedure and the need for accurate treatment planning necessitate the integration of advanced technologies. In this study, we propose the utilisation of AI to enhance the precision and efficiency of implant surgery planning for single tooth defects. A total of twenty patients with single tooth loss were enrolled. Cone-beam computed tomography (CBCT) and intra-oral scans were obtained and imported into the AI-dentist software for 3D reconstruction. AI assisted in implant selection, tooth position identification, and crown fabrication. Evaluation included subjective verification and objective assessments. A paired samples t-test was used to compare planning times (dentist vs. AI), with a significance level of p < 0.05. Twenty patients (9 male, 11 female; mean age 59.5 ± 11.86 years) with single missing teeth participated in this study. Implant margins were carefully positioned: 3.05 ± 1.44 mm from adjacent roots, 2.52 ± 0.65 mm from bone plate edges, 3.05 ± 1.44 mm from sinus/canal, and 3.85 ± 1.23 mm from gingival height. Manual planning (21.50 ± 4.87 min) was statistically significantly slower than AI (11.84 ± 3.22 min, p < 0.01). Implant planning met 100% buccolingual/proximal/distal bone volume criteria and 90% sinus/canal distance criteria. Two patients required sinus lifting and bone grafting due to insufficient bone volume. This study highlights the promising role of AI in enhancing the precision and efficiency of dental implant surgery planning for single tooth defects. Further studies are necessary to validate the effectiveness and safety of AI-assisted planning in a larger patient population.

To develop a deep learning (DL) model for predicting disease-free survival (DFS) in clinical stage I lung cancer patients who underwent surgical resection using pre-treatment CT images, and further validate it in patients receiving stereotactic body radiation therapy (SBRT). A retrospective cohort of 2489 clinical stage I non-small cell lung cancer (NSCLC) patients treated with operation (2015-2017) was enrolled to develop a DL-based DFS prediction model. Tumor features were extracted from CT images using a three-dimensional convolutional neural network. External validation was performed on 248 clinical stage I patients receiving SBRT from two hospitals. A clinical model was constructed by multivariable Cox regression for comparison. Model performance was evaluated with Harrell's concordance index (C-index), which measures the model's ability to correctly rank survival times by comparing all possible pairs of subjects. In the surgical cohort, the DL model effectively predicted DFS with a C-index of 0.85 (95% CI: 0.80-0.89) in the internal testing set, significantly outperforming the clinical model (C-index: 0.76). Based on the DL model, 68 patients in the SBRT cohort identified as high-risk had significantly worse DFS compared to the low-risk group (p < 0.01, 5-year DFS rate: 34.7% vs 77.4%). The DL-score was demonstrated to be an independent predictor of DFS in both cohorts (p < 0.01). The CT-based DL model improved DFS prediction in clinical stage I lung cancer patients, identifying populations at high risk of recurrence and metastasis to guide clinical decision-making. Question The recurrence or metastasis rate of early-stage lung cancer remains high and varies among patients following radical treatments such as surgery or SBRT. Findings This CT-based DL model successfully predicted DFS and stratified varying disease risks in clinical stage I lung cancer patients undergoing surgery or SBRT. Clinical relevance The CT-based DL model is a reliable predictive tool for the prognosis of early-stage lung cancer. Its accurate risk stratification assists clinicians in identifying specific patients for personalized clinical decision making.

Robustness against input data perturbations is essential for deploying deep learning models in clinical practice. Adversarial attacks involve subtle, voxel-level manipulations of scans to increase deep learning models' prediction errors. Testing deep learning model performance on examples of adversarial images provides a measure of robustness, and including adversarial images in the training set can improve the model's robustness. In this study, we examined adversarial training and input modifications to improve the robustness of deep learning models in predicting hematoma expansion (HE) from admission head CTs of patients with acute intracerebral hemorrhage (ICH). We used a multicenter cohort of <i>n</i> = 890 patients for cross-validation/training, and a cohort of <i>n</i> = 684 consecutive patients with ICH from 2 stroke centers for independent validation. Fast gradient sign method (FGSM) and projected gradient descent (PGD) adversarial attacks were applied for training and testing. We developed and tested 4 different models to predict ≥3 mL, ≥6 mL, ≥9 mL, and ≥12 mL HE in an independent validation cohort applying receiver operating characteristics area under the curve (AUC). We examined varying mixtures of adversarial and nonperturbed (clean) scans for training as well as including additional input from the hyperparameter-free Otsu multithreshold segmentation for model. When deep learning models trained solely on clean scans were tested with PGD and FGSM adversarial images, the average HE prediction AUC decreased from 0.8 to 0.67 and 0.71, respectively. Overall, the best performing strategy to improve model robustness was training with 5:3 mix of clean and PGD adversarial scans and addition of Otsu multithreshold segmentation to model input, increasing the average AUC to 0.77 against both PGD and FGSM adversarial attacks. Adversarial training with FGSM improved robustness against similar type attack but offered limited cross-attack robustness against PGD-type images. Adversarial training and inclusion of threshold-based segmentation as an additional input can improve deep learning model robustness in prediction of HE from admission head CTs in acute ICH.

The integration of computed tomography-derived fractional flow reserve (CT-FFR), utilizing computational fluid dynamics and artificial intelligence (AI) in routine coronary computed tomographic angiography (CCTA), presents a promising approach to enhance evaluations of functional lesion severity. Extensive evidence underscores the diagnostic accuracy, prognostic significance, and clinical relevance of CT-FFR, prompting recent clinical guidelines to recommend its combined use with CCTA for selected individuals with with intermediate stenosis on CCTA and stable or acute chest pain. This manuscript critically examines the existing clinical evidence, evaluates the diagnostic performance, and outlines future perspectives for integrating noninvasive assessments of coronary anatomy and physiology. Furthermore, it serves as a practical guide for medical imaging professionals by addressing common pitfalls and challenges associated with CT-FFR while proposing potential solutions to facilitate its successful implementation in clinical practice.

Computed tomography (CT) systems are indispensable for diagnostics but pose risks due to radiation exposure. Low-dose CT (LDCT) mitigates these risks but introduces noise and artifacts that compromise diagnostic accuracy. While deep learning methods, such as convolutional neural networks (CNNs) and generative adversarial networks (GANs), have been applied to LDCT denoising, challenges persist, including difficulties in preserving fine details and risks of model collapse. Recently, the Denoising Diffusion Probabilistic Model (DDPM) has addressed the limitations of traditional methods and demonstrated exceptional performance across various tasks. Despite these advancements, its high computational cost during training and extended sampling time significantly hinder practical clinical applications. Additionally, DDPM's reliance on random Gaussian noise can reduce optimization efficiency and performance in task-specific applications. To overcome these challenges, this study proposes a novel LDCT denoising framework that integrates the Latent Diffusion Model (LDM) with the Cold Diffusion Process. LDM reduces computational costs by conducting the diffusion process in a low-dimensional latent space while preserving critical image features. The Cold Diffusion Process replaces Gaussian noise with a CT denoising task-specific degradation approach, enabling efficient denoising with fewer time steps. Experimental results demonstrate that the proposed method outperforms DDPM in key metrics, including PSNR, SSIM, and RMSE, while achieving up to 2 × faster training and 14 × faster sampling. These advancements highlight the proposed framework's potential as an effective and practical solution for real-world clinical applications.

To develop a machine learning-based workflow for patient-specific breast radiation dosimetry in CT. Two hundred eighty-six chest CT examinations, with corresponding right and left breast contours, were retrospectively collected from the radiotherapy department at our institution to develop and validate breast segmentation U-Nets. Additionally, Monte Carlo simulations were performed for each CT scan to determine radiation doses to the breasts. The derived breast doses, along with predictors such as X-ray tube current and radiomic features, were then used to train deep neural networks (DNNs) for breast dose prediction. The breast segmentation models achieved a mean dice similarity coefficient of 0.92, with precision and sensitivity scores above 0.90 for both breasts, indicating high segmentation accuracy. The DNNs demonstrated close alignment with ground truth values, with mean predicted doses of 5.05 ± 0.50 mGy for the right breast and 5.06 ± 0.55 mGy for the left breast, compared to ground truth values of 5.03 ± 0.57 mGy and 5.02 ± 0.61 mGy, respectively. The mean absolute percentage errors were 4.01 % (range: 3.90 %-4.12 %) for the right breast and 4.82 % (range: 4.56 %-5.11 %) for the left breast. The mean inference time was 30.2 ± 4.3 s. Statistical analysis showed no significant differences between predicted and actual doses (p ≥ 0.07). This study presents an automated, machine learning-based workflow for breast radiation dosimetry in CT, integrating segmentation and dose prediction models. The models and code are available at: https://github.com/eltzanis/ML-based-Breast-Radiation-Dosimetry-in-CT.

To develop and validate a deep learning model based on three-dimensional features (DL_3D) for distinguishing lung adenocarcinoma (LUAD) from tuberculoma (TBM). A total of 1160 patients were collected from three hospitals. A vision transformer network-based DL_3D model was trained, and its performance in differentiating LUAD from TBM was evaluated using validation and external test sets. The performance of the DL_3D model was compared with that of two-dimensional features (DL_2D), radiomics, and six radiologists. Diagnostic performance was assessed using the area under the receiver operating characteristic curves (AUCs) analysis. The study included 840 patients in the training set (mean age, 54.8 years [range, 19-86 years]; 514 men), 210 patients in the validation set (mean age, 54.3 years [range, 18-86 years]; 128 men), and 110 patients in the external test set (mean age, 54.7 years [range, 22-88 years]; 51 men). In both the validation and external test sets, DL_3D exhibited excellent diagnostic performance (AUCs, 0.895 and 0.913, respectively). In the test set, the DL_3D model showed better performance (AUC, 0.913; 95% CI: 0.854, 0.973) than the DL_2D (AUC, 0.804, 95% CI: 0.722, 0.886; p < 0.001), radiomics (AUC, 0.676, 95% CI: 0.574, 0.777; p < 0.001), and six radiologists (AUCs, 0.692 to 0.810; p value range < 0.001-0.035). The DL_3D model outperforms expert radiologists in distinguishing LUAD from TBM. Question Can a deep learning model perform in differentiating LUAD from TBM on non-enhanced CT images? Findings The DL_3D model demonstrated higher diagnostic performance than the DL_2D model, radiomics model, and six radiologists in differentiating LUAD and TBM. Clinical relevance The DL_3D model could accurately differentiate between LUAD and TBM, which can help clinicians make personalized treatment plans.

The rapid advancements in natural language processing, particularly the development of large language models (LLMs), have opened new avenues for managing complex clinical text data. However, the inherent complexity and specificity of medical texts present significant challenges for the practical application of prompt engineering in diagnostic tasks. This paper explores LLMs with new prompt engineering technology to enhance model interpretability and improve the prediction performance of pulmonary disease based on a traditional deep learning model. A retrospective dataset including 2965 chest CT radiology reports was constructed. The reports were from 4 cohorts, namely, healthy individuals and patients with pulmonary tuberculosis, lung cancer, and pneumonia. Then, a novel prompt engineering strategy that integrates feature summarization (F-Sum), chain of thought (CoT) reasoning, and a hybrid retrieval-augmented generation (RAG) framework was proposed. A feature summarization approach, leveraging term frequency-inverse document frequency (TF-IDF) and K-means clustering, was used to extract and distill key radiological findings related to 3 diseases. Simultaneously, the hybrid RAG framework combined dense and sparse vector representations to enhance LLMs' comprehension of disease-related text. In total, 3 state-of-the-art LLMs, GLM-4-Plus, GLM-4-air (Zhipu AI), and GPT-4o (OpenAI), were integrated with the prompt strategy to evaluate the efficiency in recognizing pneumonia, tuberculosis, and lung cancer. The traditional deep learning model, BERT (Bidirectional Encoder Representations from Transformers), was also compared to assess the superiority of LLMs. Finally, the proposed method was tested on an external validation dataset consisted of 343 chest computed tomography (CT) report from another hospital. Compared with BERT-based prediction model and various other prompt engineering techniques, our method with GLM-4-Plus achieved the best performance on test dataset, attaining an F1-score of 0.89 and accuracy of 0.89. On the external validation dataset, F1-score (0.86) and accuracy (0.92) of the proposed method with GPT-4o were the highest. Compared to the popular strategy with manually selected typical samples (few-shot) and CoT designed by doctors (F1-score=0.83 and accuracy=0.83), the proposed method that summarized disease characteristics (F-Sum) based on LLM and automatically generated CoT performed better (F1-score=0.89 and accuracy=0.90). Although the BERT-based model got similar results on the test dataset (F1-score=0.85 and accuracy=0.88), its predictive performance significantly decreased on the external validation set (F1-score=0.48 and accuracy=0.78). These findings highlight the potential of LLMs to revolutionize pulmonary disease prediction, particularly in resource-constrained settings, by surpassing traditional models in both accuracy and flexibility. The proposed prompt engineering strategy not only improves predictive performance but also enhances the adaptability of LLMs in complex medical contexts, offering a promising tool for advancing disease diagnosis and clinical decision-making.

<b>Background:</b> Studies of artificial intelligence (AI) for lung nodule detection on CT have primarily been conducted in investigational settings and/or focused on lung cancer screening. <b>Objective:</b> To evaluate the impact of AI assistance on radiologists' diagnostic performance for detecting lung metastases on chest CT in patients with colorectal cancer (CRC) in real-world clinical practice and to assess the clinical utility of AI assistance in this setting. <b>Methods:</b> This retrospective study included patients with CRC who underwent chest CT as surveillance for lung metastasis from May 2020 to December 2020 (conventional interpretation) or May 2022 to December 2022 (AI-assisted interpretation). Between periods, the institution implemented a commercial AI lung nodule detection system. During the second period, radiologists interpreted examinations concurrently with AI-generated reports, using clinical judgment regarding whether to report AI-detected nodules. The reference standard for metastasis incorporated pathologic and clinical follow-up criteria. Diagnostic performance (sensitivity, specificity, accuracy), and clinical utility (diagnostic yield, false-referral rate, management changes after positive reports) were compared between groups based on clinical radiology reports. Net benefit was estimated using decision curve analysis equation. Standalone AI interpretation was evaluated. <b>Results:</b> The conventional interpretation group included 647 patients (mean age, 64±11 years; 394 men, 253 women; metastasis prevalence, 4.3%); AI-assisted interpretation group included 663 patients (mean age, 63±12 years; 381 men, 282 women; metastasis prevalence, 4.4%). The AI-assisted interpretation group compared with the conventional interpretation group showed higher sensitivity (72.4% vs 32.1%; p=.008), accuracy (98.5% vs 96.0%; p=.005), and frequency of management changes (55.2% vs 25.0%, p=.02), without significant difference in specificity (99.7% vs 98.9%; p=.11), diagnostic yield (3.2% vs 1.4%, p=.30) or false-referral rate (0.3% vs 1.1%, p=.10). AI-assisted interpretation had positive estimated net benefit across outcome ratios. Standalone AI correctly detected metastasis in 24 of 29 patients but had 381 false-positive detections in 634 patients without metastasis; only one AI false-positive was reported as positive by interpretating radiologists. <b>Conclusion:</b> AI assistance yielded increased sensitivity, accuracy, and frequency of management changes, without significantly changed specificity. False-positive AI results minimally impacted radiologists' interpretations. <b>Clinical Impact:</b> The findings support clinical utility of AI assistance for CRC metastasis surveillance.