This study presents a deep learning system for breast cancer detection in mammography, developed using a modified EfficientNetV2 architecture with enhanced attention mechanisms. The model was trained on mammograms from a major Thai medical center and validated on three distinct datasets: an in-domain test set (9,421 cases), a biopsy-confirmed set (883 cases), and an out-of-domain generalizability set (761 cases) collected from two different hospitals. For cancer detection, the model achieved AUROCs of 0.89, 0.96, and 0.94 on the respective datasets. The system's lesion localization capability, evaluated using metrics including Lesion Localization Fraction (LLF) and Non-Lesion Localization Fraction (NLF), demonstrated robust performance in identifying suspicious regions. Clinical validation through concordance tests showed strong agreement with radiologists: 83.5% classification and 84.0% localization concordance for biopsy-confirmed cases, and 78.1% classification and 79.6% localization concordance for out-of-domain cases. Expert radiologists' acceptance rate also averaged 96.7% for biopsy-confirmed cases, and 89.3% for out-of-domain cases. The system achieved a System Usability Scale score of 74.17 for source hospital, and 69.20 for validation hospitals, indicating good clinical acceptance. These results demonstrate the model's effectiveness in assisting mammogram interpretation, with the potential to enhance breast cancer screening workflows in clinical practice.

Cardiovascular magnetic resonance (CMR) cine imaging is the gold standard for assessing ventricular volumes and function. It typically requires two-dimensional (2D) bSSFP sequences and multiple breath-holds, which can be challenging for patients with limited breath-holding capacity. Three-dimensional (3D) cardiovascular magnetic resonance angiography (MRA) usually suffers from lengthy acquisition. Free-running 3D cine imaging with deep learning (DL) reconstruction offers a potential solution by acquiring both cine and angiography simultaneously. To evaluate the efficiency and accuracy of a ferumoxytol-enhanced 3D cine imaging MR sequence combined with DL reconstruction and Heart-NAV technology in patients with congenital heart disease. This Institutional Review Board approved this prospective study that compared (i) functional and volumetric measurements between 3 and 2D cine images; (ii) contrast-to-noise ratio (CNR) between deep-learning (DL) and compressed sensing (CS)-reconstructed 3D cine images; and (iii) cross-sectional area (CSA) measurements between DL-reconstructed 3D cine images and the clinical 3D MRA images acquired using the bSSFP sequence. Paired t-tests were used to compare group measurements, and Bland-Altman analysis assessed agreement in CSA and volumetric data. Sixteen patients (seven males; median age 6 years) were recruited. 3D cine imaging showed slightly larger right ventricular (RV) volumes and lower RV ejection fraction (EF) compared to 2D cine, with a significant difference only in RV end-systolic volume (P = 0.02). Left ventricular (LV) volumes and EF were slightly higher, and LV mass was lower, without significant differences (P ≥ 0.05). DL-reconstructed 3D cine images showed significantly higher CNR in all pulmonary veins than CS-reconstructed 3D cine images (all P < 0.05). Highly accelerated free-running 3D cine imaging with DL reconstruction shortens acquisition times and provides comparable volumetric measurements to 2D cine, and comparable CSA to clinical 3D MRA.

This study aims to assess the added value of utilizing short-dynamic whole-body PET/CT scans and implementing motion correction before quantifying metabolic rate, offering more insights into physiological processes. While this approach may not be commonly adopted, addressing motion effects is crucial due to their demonstrated potential to cause significant errors in parametric imaging. A 15-minute dynamic FDG PET acquisition protocol was utilized for four lymphoma patients undergoing therapy evaluation. Parametric imaging was obtained using a population-based input function (PBIF) derived from twelve patients with full 65-minute dynamic FDG PET acquisition. AI-based registration methods were employed to correct misalignments between both PET and ACCT and PET-to-PET. Tumour characteristics were assessed using both parametric images and standardized uptake values (SUV). The motion correction process significantly reduced mismatches between images without significantly altering voxel intensity values, except for SUV_max. Following the alignment of the attenuation correction map with the PET frame, an increase in SUV_max in FDG-avid lymph nodes was observed, indicating its susceptibility to spatial misalignments. In contrast, Patlak K_i parameter was highly sensitive to misalignment across PET frames, that notably altered the Patlak slope. Upon completion of the motion correction process, the parametric representation revealed heterogeneous behaviour among lymph nodes compared to SUV images. Notably, reduced volume of elevated metabolic rate was determined in the mediastinal lymph nodes in contrast with an SUV of 5 g/ml, indicating potential perfusion or inflammation. Motion resolved short-dynamic PET can enhance the utility and reliability of parametric imaging, an aspect often overlooked in commercial software.

The utilization of magnetic resonance (MR) im-aging to guide near-infrared spectral tomography (NIRST) shows significant potential for improving the specificity and sensitivity of breast cancer diagnosis. However, the ef-ficiency and accuracy of NIRST image reconstruction have been limited by the complexities of light propagation mod-eling and MRI image segmentation. To address these chal-lenges, we developed and evaluated a deep learning-based approach for MR-guided 3D NIRST image reconstruction (DL-MRg-NIRST). Using a network trained on synthetic data, the DL-MRg-NIRST system reconstructed images from data acquired during 38 clinical imaging exams of pa-tients with breast abnormalities. Statistical analysis of the results demonstrated a sensitivity of 87.5%, a specificity of 92.9%, and a diagnostic accuracy of 89.5% in distinguishing pathologically defined benign from malignant lesions. Ad-ditionally, the combined use of MRI and DL-MRg-NIRST di-agnoses achieved an area under the receiver operating characteristic (ROC) curve of 0.98. Remarkably, the DL-MRg-NIRST image reconstruction process required only 1.4 seconds, significantly faster than state-of-the-art MR-guided NIRST methods.

Permanent prostate brachytherapy has inherent intraoperative organ deformation due to the inflatable trans-rectal ultrasound probe cover. Since the majority of the dose is delivered postoperatively with no deformation, the dosimetry approved at the time of implant may not accurately represent the dose delivered to the target and organs at risk. We aimed to evaluate the biological effect of the prostate deformation and its correlation with patient-reported outcomes. We prospectively acquired ultrasound images of the prostate pre- and postprobe cover inflation for 27 patients undergoing I-125 seed implant. The coordinates of implanted seeds from approved clinical plan were transferred to deformation-corrected prostate to simulate the actual dosimetry using a machine learning-based deformable image registration. The DVHs of both sets of plans were reduced to biologically effective dose (BED) distribution and subsequently to Tumor Control Probability (TCP) and Normal Tissue Complication Probability (NTCP) metrics. The change in fourteen patient-reported rectal and urinary symptoms between pretreatment to 6 months post-op time points were correlated with the TCP and NTCP metrics using the area under the curve (AUC) and odds ratio (OR). Between the clinical and the deformation corrected research plans, the mean TCP decreased by 9.4% (p < 0.01), whereas mean NTCP of rectum decreased by 10.3% and that of urethra increased by 16.3%, respectively (p < 0.01). For the diarrhea symptom, the deformation corrected research plans showed AUC=0.75 and OR = 8.9 (1.3-58.8) for the threshold NTCP>20%, while the clinical plan showed AUC=0.56 and OR = 1.4 (0.2 to 9.0). For the symptom of urinary control, the deformation corrected research plans showed AUC = 0.70, OR = 6.9 (0.6 to 78.0) for the threshold of NTCP>15%, while the clinical plan showed AUC = 0.51 and no positive OR. Taking organ deformation into consideration, clinical brachytherapy plans showed worse tumor coverage, worse urethra sparing but better rectal sparing. The deformation corrected research plans showed a stronger correlation with the patient-reported outcome than the clinical plans for the symptoms of diarrhea and urinary control.

Early screening, diagnosis, and treatment of lung cancer are pivotal in clinical practice since the tumor stage remains the most dominant factor that affects patient survival. Previous initiatives have tried to develop new tools for decision-making of lung cancer. In this study, we proposed the China Protocol, a complete workflow of lung cancer tailored to the Chinese population, which is implemented by steps including early screening by evaluation of risk factors and three-dimensional thin-layer image reconstruction technique for low-dose computed tomography (Tre-LDCT), accurate diagnosis via artificial intelligence (AI) and novel biomarkers, and individualized treatment through non-invasive molecule visualization strategies. The application of this protocol has improved the early diagnosis and 5-year survival rates of lung cancer in China. The proportion of early-stage (stage I) lung cancer has increased from 46.3% to 65.6%, along with a 5-year survival rate of 90.4%. Moreover, especially for stage IA1 lung cancer, the diagnosis rate has improved from 16% to 27.9%; meanwhile, the 5-year survival rate of this group achieved 97.5%. Thus, here we defined stage IA1 lung cancer, which cohort benefits significantly from early diagnosis and treatment, as the "ultra-early stage lung cancer", aiming to provide an intuitive description for more precise management and survival improvement. In the future, we will promote our findings to multicenter remote areas through medical alliances and mobile health services with the desire to move forward the diagnosis and treatment of lung cancer.

This is a multi-institutional study to evaluate a head-and-neck CT auto-segmentation software across seven institutions globally. 11 lymph node levels and 7 organs-at-risk contours were evaluated in a two-phase study design. Time savings were measured in both phases, and the inter-observer variability across the seven institutions was quantified in phase two. Overall time savings were found to be 42% in phase one and 49% in phase two. Lymph node levels IA, IB, III, IVA, and IVB showed no significant time savings, with some centers reporting longer editing times than manual delineation. All the edited ROIs showed reduced inter-observer variability compared to manual segmentation. Our study shows that auto-segmentation plays a crucial role in harmonizing contouring practices globally. However, the clinical benefits of auto-segmentation software vary significantly across ROIs and between clinics. To maximize its potential, institution-specific commissioning is required to optimize the clinical benefits.

A critical limitation to deployment and utilization of Artificial Intelligence (AI) algorithms in radiology practice is the actual integration of algorithms directly into the clinical Picture Archiving and Communications Systems (PACS). Here, we sought to integrate an AI-based pipeline for prostate organ and intraprostatic lesion segmentation within a clinical PACS environment to enable point-of-care utilization under a prospective clinical trial scenario. A previously trained, publicly available AI model for segmentation of intra-prostatic findings on multiparametric Magnetic Resonance Imaging (mpMRI) was converted into a containerized environment compatible with MONAI Deploy Express. An inference server and dedicated clinical PACS workflow were established within our institution for evaluation of real-time use of the AI algorithm. PACS-based deployment was prospectively evaluated in two phases: first, a consecutive cohort of patients undergoing diagnostic imaging at our institution and second, a consecutive cohort of patients undergoing biopsy based on mpMRI findings. The AI pipeline was executed from within the PACS environment by the radiologist. AI findings were imported into clinical biopsy planning software for target definition. Metrics analyzing deployment success, timing, and detection performance were recorded and summarized. In phase one, clinical PACS deployment was successfully executed in 57/58 cases and were obtained within one minute of activation (median 33 s [range 21-50 s]). Comparison with expert radiologist annotation demonstrated stable model performance compared to independent validation studies. In phase 2, 40/40 cases were successfully executed via PACS deployment and results were imported for biopsy targeting. Cancer detection rates for prostate cancer were 82.1% for ROI targets detected by both AI and radiologist, 47.8% in targets proposed by AI and accepted by radiologist, and 33.3% in targets identified by the radiologist alone. Integration of novel AI algorithms requiring multi-parametric input into clinical PACS environment is feasible and model outputs can be used for downstream clinical tasks.

To develop and validate a machine learning model based on dual-energy computed tomography (DECT) for predicting cervical lymph node metastases (CLNM) in patients diagnosed with nasopharyngeal carcinoma (NPC). This prospective single-center study enrolled patients with NPC and the study assessment included both DECT and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT). Radiomics features were extracted from each region of interest (ROI) for cervical lymph nodes using arterial and venous phase images at 100 keV and 150 keV, either individually as non-fusion models or combined as fusion models on the DECT images. The performance of the random forest (RF) models, combined with radiomics features, was evaluated by area under the receiver operating characteristic curve (AUC) analysis. DeLong's test was employed to compare model performances, while decision curve analysis (DCA) assessed the clinical utility of the predictive models. Sixty-six patients with NPC were included for analysis, which was divided into a training set (n = 42) and a validation set (n = 22). A total of 13 radiomic models were constructed (4 non-fusion models and 9 fusion models). In the non-fusion models, when the threshold value exceeded 0.4, the venous phase at 100 keV (V100) (AUC, 0.9667; 95 % confidence interval [95 % CI], 0.9363-0.9901) model exhibited a higher net benefit than other non-fusion models. The V100 + V150 fusion model achieved the best performance, with an AUC of 0.9697 (95 % CI, 0.9393-0.9907). DECT-based radiomics effectively diagnosed CLNM in patients with NPC and may potentially be a valuable tool for clinical decision-making. This study improved pre-operative evaluation, treatment strategy selection, and prognostic evaluation for patients with nasopharyngeal carcinoma by combining DECT and radiomics to predict cervical lymph node status prior to treatment.

To investigate whether the deep learning reconstruction (DLR) combined with contrast-enhancement-boost (CE-boost) technique can improve the diagnostic quality of CT pulmonary angiography (CTPA) at low radiation and contrast doses, compared with routine CTPA using hybrid iterative reconstruction (HIR). This prospective two-center study included 130 patients who underwent CTPA for suspected pulmonary embolism. Patients were randomly divided into two groups: the routine CTPA group, reconstructed using HIR; and the dual-low dose CTPA group, reconstructed using HIR and DLR, additionally combined with the CE-boost to generate HIR-boost and DLR-boost images. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of pulmonary arteries were quantitatively assessed. Two experienced radiologists independently ordered CT images (5, best; 1, worst) based on overall image noise and vascular contrast. Diagnostic performance for PE detection was calculated for each dataset. Patient demographics were similar between groups. Compared to HIR images of the routine group, DLR-boost images of the dual-low dose group were significantly better at qualitative scores (p < 0.001). The CT values of pulmonary arteries between the DLR-boost and the HIR images were comparable (p > 0.05), whereas the SNRs and CNRs of pulmonary arteries in the DLR-boost images were the highest among all five datasets (p < 0.001). The AUCs of DLR, HIR-boost, and DLR-boost were 0.933, 0.924, and 0.986, respectively (all p > 0.05). DLR combined with CE-boost technique can significantly improve the image quality of CTPA with reduced radiation and contrast doses, facilitating a more accurate diagnosis of pulmonary embolism. Question The dual-low dose protocol is essential for detecting pulmonary emboli (PE) in follow-up CT pulmonary angiography (PA), yet effective solutions are still lacking. Findings Deep learning reconstruction (DLR)-boost with reduced radiation and contrast doses demonstrated higher quantitative and qualitative image quality than hybrid-iterative reconstruction in the routine CTPA. Clinical relevance DLR-boost based low-radiation and low-contrast-dose CTPA protocol offers a novel strategy to further enhance the image quality and diagnosis accuracy for pulmonary embolism patients.