A flexible deep learning framework for liver tumor diagnosis using variable multi-phase contrast-enhanced CT scans.

BACKGROUND: Liver cancer is a significant cause of cancer-related mortality worldwide and requires tailored treatment strategies for different types. However, preoperative accurate diagnosis of the type presents a challenge. This study aims to develop an automatic diagnostic model based on multi-phase contrast-enhanced CT (CECT) images to distinguish between hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and normal individuals. METHODS: We designed a Hierarchical Long Short-Term Memory (H-LSTM) model, whose core components consist of a shared image feature extractor across phases, an internal LSTM for each phase, and an external LSTM across phases. The internal LSTM aggregates features from different layers of 2D CECT images, while the external LSTM aggregates features across different phases. H-LSTM can handle incomplete phases and varying numbers of CECT image layers, making it suitable for real-world decision support scenarios. Additionally, we applied phase augmentation techniques to process multi-phase CECT images, improving the model's robustness. RESULTS: The H-LSTM model achieved an overall average AUROC of 0.93 (0.90, 1.00) on the test dataset, with AUROC for HCC classification reaching 0.97 (0.93, 1.00) and for ICC classification reaching 0.90 (0.78, 1.00). Comprehensive validation in scenarios with incomplete phases was performed, with the H-LSTM model consistently achieving AUROC values over 0.9. CONCLUSION: The proposed H-LSTM model can be employed for classification tasks involving incomplete phases of CECT images in real-world scenarios, demonstrating high performance. This highlights the potential of AI-assisted systems in achieving accurate diagnosis and treatment of liver cancer. H-LSTM offers an effective solution for processing multi-phase data and provides practical value for clinical diagnostics.

Indian Hand Anthropometry: Computer Tomography-Based Morphometric Analysis of Metacarpal.

Introduction Metacarpal fractures are common and have various treatment options, but understanding their morphometry is crucial for optimizing fixation techniques and reducing complications. Accurate assessment of metacarpal anatomy is challenging in conventional radiographs but feasible with computed tomography (CT) scans, which offer precise views. This study aimed to provide accurate anatomical data on metacarpals within an Indian population using CT scans and to compare the results with existing literature. The findings have implications for surgical procedures, including plating, pinning, and intramedullary screw fixation. Materials and Methods This retrospective analysis utilized CT scans of 100 hands, including 50 males and 50 females, from two hospitals in India. Inclusion criteria included complete metacarpal visualization with a slice thickness of 0.6 mm, while exclusion criteria involved trauma, deformity, or underlying pathologies. Various parameters of all metacarpals were measured using RadiAnt DICOM Viewer 2021.1, providing accurate anteroposterior and lateral views. Results Male and female cohorts had mean ages of 38.58 ± 12.02 and 43.60 ± 13.61 years, respectively. The study showed good to excellent reliability in measurements. The 2nd metacarpal was consistently the longest, and the general length pattern was 3rd > 4th > 5th > 1st metacarpal in both genders. Men generally had larger metacarpal dimensions than women, except for intramedullary diameter, which showed minimal sex-related differences. Notably, the medullary cavity's narrowest part was at the 4th metacarpal, and the thumb had the widest intramedullary diameter. Conclusion This study provides valuable anatomical reference data for metacarpals in an Indian population, aiding in optimizing surgical techniques for metacarpal fractures. The 2nd metacarpal consistently stood out as the longest, and men generally had larger metacarpal dimensions than women. These insights into anatomical variations can inform clinical decisions and stimulate further research in this field. However, a larger and more diverse sample would enhance the study's representativeness.

Applications of Artificial Intelligence-Based Systems in the Management of Esophageal Varices.

BACKGROUND: Esophageal varices, dilated submucosal veins in the lower esophagus, are commonly associated with portal hypertension, particularly due to liver cirrhosis. The high morbidity and mortality linked to variceal hemorrhage underscore the need for accurate diagnosis and effective management. The traditional method of assessing esophageal varices is esophagogastroduodenoscopy (EGD), which, despite its diagnostic and therapeutic capabilities, presents limitations such as interobserver variability and invasiveness. This review aims to explore the role of artificial intelligence (AI) in enhancing the management of esophageal varices, focusing on its applications in diagnosis, risk stratification, and treatment optimization. METHODS: This systematic review focuses on the capabilities of AI algorithms to analyze clinical scores, laboratory data, endoscopic images, and imaging modalities like CT scans. RESULTS: AI-based systems, particularly machine learning (ML) and deep learning (DL) algorithms, have demonstrated the ability to improve risk stratification and diagnosis of esophageal varices, analyzing vast amounts of data, identifying patterns, and providing individualized recommendations. However, despite these advancements, clinical scores based on laboratory data still show low specificity for esophageal varices, often requiring confirmatory endoscopic or imaging studies. CONCLUSIONS: AI integration in managing esophageal varices offers significant potential for advancing diagnosis, risk assessment, and treatment strategies. While promising, AI systems should complement rather than replace traditional methods, ensuring comprehensive patient evaluation. Further research is needed to refine these technologies and validate their efficacy in clinical practice.

Radiological examination of greater palatine canal medial wall dehiscence.

INTRODUCTION: The greater palatine canal (GPC) connects the pterygopalatine fossa to the greater palatine foramen and houses vital neurovascular structures, which provide sensory innervation and circulation to the gums, palate, and nasal cavity. The GPC is of great clinical importance to various medical specialties; however, the anatomical variability of the GPC poses a risk of iatrogenic injury and complications. Therefore, understanding the normal anatomy and variations of the GPC is crucial for identifying vital structures and minimizing risks in clinical practice. PURPOSE: The aim was to fill a gap in the current literature by focusing on the prevalence of GPC medial wall dehiscence, a lesser-known anatomic variation, in radiological scans. METHODS: A total of 200 head and neck CT scans were examined, where 71 scans met the inclusion criteria. Statistical significance for incidence of GPC medial wall dehiscence, in reference to sex and side, was measured. RESULTS: The GPC medial wall dehiscence was observed in 69% of scans. Bilateral dehiscence was seen in 57.7% of scans, while right-sided and left-sided unilateral dehiscence were found in 14.1% and 11.3%, respectively. Significant difference was found between the incidence of bilateral dehiscence compared to the absence of dehiscence. CONCLUSION: Previous studies have highlighted the potential risks associated with invasive procedures involving the GPC. The clinical relevance of GPC medial wall dehiscence lies in the increased risk of transecting the contained neurovascular bundle. The presence of dehiscence emphasizes the need for meticulous preoperative radiologic analysis to tailor surgical approaches to individual patient anatomy.

Enhancing liver tumor segmentation with UNet-ResNet: Leveraging ResNet's power.

BACKGROUND: Liver cancer poses a significant health challenge due to its high incidence rates and complexities in detection and treatment. Accurate segmentation of liver tumors using medical imaging plays a crucial role in early diagnosis and treatment planning. OBJECTIVE: This study proposes a novel approach combining U-Net and ResNet architectures with the Adam optimizer and sigmoid activation function. The method leverages ResNet's deep residual learning to address training issues in deep neural networks. At the same time, U-Net's structure facilitates capturing local and global contextual information essential for precise tumor characterization. The model aims to enhance segmentation accuracy by effectively capturing intricate tumor features and contextual details by integrating these architectures. The Adam optimizer expedites model convergence by dynamically adjusting the learning rate based on gradient statistics during training. METHODS: To validate the effectiveness of the proposed approach, segmentation experiments are conducted on a diverse dataset comprising 130 CT scans of liver cancers. Furthermore, a state-of-the-art fusion strategy is introduced, combining the robust feature learning capabilities of the UNet-ResNet classifier with Snake-based Level Set Segmentation. RESULTS: Experimental results demonstrate impressive performance metrics, including an accuracy of 0.98 and a minimal loss of 0.10, underscoring the efficacy of the proposed methodology in liver cancer segmentation. CONCLUSION: This fusion approach effectively delineates complex and diffuse tumor shapes, significantly reducing errors.

Morphological and morphometric analysis of the Sacral Hiatus using lumbosacral spine CT scans: clinical relevance in Caudal Epidural Analgesia.

BACKGROUND: To conduct a morphological and morphometric analysis of the sacral hiatus (SH) using lumbosacral spine CT scans and to evaluate its clinical relevance in caudal epidural analgesia (CEA). MATERIALS AND METHODS: This retrospective study analyzed 77 lumbosacral spine CT scans from a diverse patient population. The shape of the SH was classified into common types: inverted U, inverted V, irregular, and bilobed. Morphometric measurements included the length, width, and depth at the apex of the SH. The apex level of the SH was also determined in relation to the sacral vertebrae, and statistical analysis was performed to identify any correlation between the apex level and the morphometric dimensions. RESULTS: The most frequent SH shape was inverted U (68.83%), followed by inverted V (20.77%), irregular (9%), and a single instance of a bilobed shape (1.29%). The apex of the SH was most commonly located at the level of the S4 vertebra (75.32%), followed by the S3 vertebra (20.77%), S5 in two (2.59) and S2 in one (1.29%). No significant correlation was found between the level of the apex and the length, width, or depth of the SH. These findings indicate a high degree of anatomical variability in the SH, independent of the apex level. CONCLUSIONS: The anatomical variability of the SH, as observed in this study, underscores the need for individualized assessment during CEA. The lack of correlation between the apex level and the morphometric dimensions of the SH highlights the importance of imaging modalities such as ultrasound or fluoroscopy to ensure precise localization and effective analgesia administration. These insights can improve clinical outcomes by enhancing the accuracy and safety of caudal epidural procedures.

Effects of Iodinated Contrast-enhanced CT on Urinary Iodine Levels in Postoperative Patients with Differentiated Thyroid Cancer.

AIMS: This study aims to observe the fluctuating urine iodine levels in patients with differentiated thyroid cancer (DTC) following iodinated contrastenhanced computed tomography (eCT) scans. BACKGROUND: The presence of iodine in iodinated contrast agents (ICAs) can impede the effectiveness of radioactive iodine treatment (RAIT) and diagnostic scans in individuals diagnosed with DTC, as it can engage in competitive interactions with 131I. According to established guidelines, it is recommended to postpone RAIT for a period of three to four months in individuals who have had prior exposure to ICAS. The measurement of spot urine iodine concentration is a valuable indicator for assessing the overall iodine content throughout the body. OBJECTIVE: The objective is to identify the optimal timing for administering postoperative RAIT in DTC patients. METHODS: At various time points after surgery, a cohort of 467 random urine samples (126 male samples, 341 female samples, age (45±12 years)) was obtained from 269 DTC patients. The samples were analyzed for urinary iodine and urinary creatinine levels, and the urinary iodine/urine creatinine ratio (I/Cr) was computed. All samples were divided into two groups according to whether eCT before operation: the non-enhanced CT (eCT-) group and the enhanced CT (eCT+) group. The urine samples in the eCT- group were categorized into four subgroups according to the duration of strict low iodine diet (LID): (eCT-I+) no LID; (eCT-I-2W) 2 weeks of LID; (eCT-I-4W) 4 weeks of LID; and (eCT-I-6W) 6 weeks of LID. The last three groups were merged into the eCT- and effective LID group (eCT- I-). The urine samples from the eCT+ group were categorized into five subgroups: (0.5M eCT+)0.5 month after eCT+; (1M eCT+)1 month after eCT+; (2M eCT+) 2 months after eCT+; (3M eCT+) 3 months after eCT+; (≥4M eCT+) ≥4 months after eCT+. In addition, the patients within 2 months after eCT+ were divided into 2 groups according to their LID: no effective LID group (eCT+ I+) and effective LID group (eCT+ I-). Utilizing the Kruskal-Wallis and Mann-Whitney U rank sum tests, the differences in I/Cr between groups were compared. RESULTS: In the eCT-group, the I/Cr ratios of eCT-I-2W, eCT-I-4W, and eCT-I-6W were significantly lower than those of eCT-I+ (χ2 values: 4.607.99, all P 0.05). However, there was no significant difference in I/Cr between eCT-I-2W, eCT- I-4W, and eCT-I-6W (2 values: 0.591.31, all P > 0.05). Significantly higher I/Cr values were observed in 0.5M eCT+ and 1M eCT+ than in eCT-I+ (χ2 values: 3.22 and 2.18, respectively, all P<0.05). There was no significant difference in I/Cr between 2M eCT+ and eCT-I+ (χ2 = 0.76, P = 0.447). The I/Cr rations of 3M eCT+, ≥4M eCT+ were not significantly different with eCT-I- (χ2 values: 1.76; 0.58; all P > 0.05). However, they were considerably lower than eCT-I+ (χ2 values: 7.03; 5.22; all P<0.05). The I/Cr for patients who underwent eCT within two months (eCT+ I-, eCT+ I+) did not differ significantly (χ2 = 1.79, P = 0.073). CONCLUSION: For patients who are considering receiving radioactive iodine therapy (RAIT) following a diagnosis of differentiated thyroid cancer (DTC), it is recommended that the interval between RAIT treatment and enhanced computed tomography [eCT] scans be conducted at least three months.

mm3DSNet: multi-scale and multi-feedforward self-attention 3D segmentation network for CT scans of hepatobiliary ducts.

Image segmentation is a key step of the 3D reconstruction of the hepatobiliary duct tree, which is significant for preoperative planning. In this paper, a novel 3D U-Net variant is designed for CT image segmentation of hepatobiliary ducts from the abdominal CT scans, which is composed of a 3D encoder-decoder and a 3D multi-feedforward self-attention module (MFSAM). To well sufficient semantic and spatial features with high inference speed, the 3D ConvNeXt block is designed as the 3D extension of the 2D ConvNeXt. To improve the ability of semantic feature extraction, the MFSAM is designed to transfer the semantic and spatial features at different scales from the encoder to the decoder. Also, to balance the losses for the voxels and the edges of the hepatobiliary ducts, a boundary-aware overlap cross-entropy loss is proposed by combining the cross-entropy loss, the Dice loss, and the boundary loss. Experimental results indicate that the proposed method is superior to some existing deep networks as well as the radiologist without rich experience in terms of CT segmentation of hepatobiliary ducts, with a segmentation performance of 76.54% Dice and 6.56 HD.

Computed Tomography Signs of Sinonasal Inflammation in Patients with Acute Dacryocystitis.

BACKGROUND: Nasal and paranasal sinus abnormalities may be related to nasolacrimal duct obstructive disease but are strongly debated. Data of acute disease stage are lacking. OBJECTIVE: The purpose of this study was to determine if there are correlations between radiologic signs of sinus inflammation and acute dacryocystitis (AD). METHODS: This cross-sectional controlled study was conducted at Wenzhou, Zhejiang Province, China from February 2021 to November 2023. Forty-four consecutive patients with AD and 50 consecutive patients with orbital tumors (the control group), who completed preoperative computed tomography scans, were enrolled to evaluate the extent of their inflammatory sinonasal disease by the modified Lund-Mackay score system. RESULTS: The inflammation signs of the paranasal sinuses (total mean sinus scores, 95% CI [0.00, 2.00]; P < 0.001), namely the anterior ethmoid sinus(95% CI [0.00, 1.00]; P < 0.001), the posterior ethmoid sinus(95% CI [0.00, 0.00]; P = 0.003), the frontal sinus (95% CI [0.00, 0.00]; P = 0.02), and the ostiomeatal complex (P < 0.001) were more extensive in patients with AD when compared with the controls. The disease course was negatively correlated with the anterior ethmoid (P = 0.03) and frontal scores (P = 0.01). The symptom of eyelid swelling was positively correlated with the anterior ethmoid (P = 0.03), ostiomeatal complex (P = 0.004), and total sinus scores (P = 0.005). CONCLUSION: Inflammatory sinus disease was found to be more frequent in patients with AD, which was gradually alleviated with the prolongation of the disease course. The mutual spread of inflammation particularly in the acute course may play an important role in lacrimal duct obstructive disease.

The Value of Radiomics Features Based on HRCT in Predicting whether the Lung Sub-Centimeter Pure Ground Glass Nodule is Benign or Malignant.

OBJECTIVE: In this study, a radiomics model was created based on High-Resolution Computed Tomography (HRCT) images to noninvasively predict whether the sub-centimeter pure Ground Glass Nodule (pGGN) is benign or malignant. METHODS: A total of 235 patients (251 sub-centimeter pGGNs) who underwent preoperative HRCT scans and had postoperative pathology results were retrospectively evaluated. The nodules were randomized in a 7:3 ratio to the training (n=175) and the validation cohort (n=76). The volume of interest was delineated in the thin-slice lung window, from which 1316 radiomics features were extracted. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to select the radiomics features. Univariate and multivariable logistic regression were used to evaluate the independent risk variables. The performance was assessed by obtaining Receiver Operating Characteristic (ROC) curves for the clinical, radiomics, and combined models, and then the Decision Curve Analysis (DCA) assessed the clinical applicability of each model. RESULTS: Sex, volume, shape, and intensity mean were chosen by univariate analysis to establish the clinical model. Two radiomics features were retained by LASSO regression to build the radiomics model. In the training cohort, the Area Under the Curve (AUC) of the radiomics (AUC=0.844) and combined model (AUC=0.871) was higher than the clinical model (AUC=0.773). In evaluating whether or not the sub-centimeter pGGN is benign, the DCA demonstrated that the radiomics and combined model had a greater overall net benefit than the clinical model. CONCLUSION: The radiomics model may be useful in predicting the benign and malignant sub-centimeter pGGN before surgery.

Evaluating the Accuracy and Impact of the ESR-iGuide Decision Support Tool in Optimizing CT Imaging Referral Appropriateness.

Radiology referral quality impacts patient care, yet factors influencing quality are poorly understood. This study assessed the quality of computed tomography (CT) referrals, identified associated characteristics, and evaluated the ESR-iGuide clinical decision support tool's ability to optimize referrals. A retrospective review analyzed 300 consecutive CT referrals from an acute care hospital. Referral quality was evaluated on a 5-point scale by three expert reviewers (inter-rater reliability κ = 0.763-0.97). The ESR-iGuide tool provided appropriateness scores and estimated radiation exposure levels for the actual referred exams and recommended exams. Scores were compared between actual and recommended exams. Associations between ESR-iGuide scores and referral characteristics, including the specialty of the ordering physician (surgical vs. non-surgical), were explored. Of the referrals, 67.1% were rated as appropriate. The most common exams were head and abdomen/pelvis CTs. The ESR-iGuide deemed 70% of the actual referrals "usually appropriate" and found that the recommended exams had lower estimated radiation exposure compared to the actual exams. Logistic regression analysis showed that non-surgical physicians were more likely to order inappropriate exams compared to surgical physicians. Over one-third of the referrals showed suboptimal quality in the unstructured system. The ESR-iGuide clinical decision support tool identified opportunities to optimize appropriateness and reduce radiation exposure. Implementation of such a tool warrants consideration to improve communication and maximize patient care quality.

Anatomical Measurement as a Reference for Functional Endoscopic Sinus Surgery Based on CT Scans and Dissections.

Introduction Diseases of the paranasal sinuses, nasal cavities, and those related to the skull base can be treated with nasal endoscopic surgery. Anatomical references are essential to safely perform these surgeries. Objective To measure and compare the distance from the posterior wall of the maxillary sinus to the anterior skull base in cadavers and on computed tomography (CT) scans to determine a measurement as an anatomical reference in imaging exams for sinus and anterior skull base surgery. Methods In dissections and CT scans, we took measurements from the most upper and medial point of the posterior wall of the maxillary sinus (point A) to the point where the skull base deflects and the anterior sphenoid wall is formed (Δ 90°; point B), in the right and left nasal cavities. We used 51 cadavers aged ≥ 18 years in the present research. Results The measurements obtained from CT scans and dissections were greater than 1.5 cm in all cadavers, and they were positively correlated. The 1-cm increase in the AB-tomography measurement corresponded to the 1.08-cm increase to the right and 1.07-cm to the left in the AB-dissection measurement. Conclusion The CT measurements may be considered a reliable tool to promote safe and effective access to the paranasal sinuses, matching the distance that should be dissected until the anterior base of the skull.

Automated segmentation and deep learning classification of ductopenic parotid salivary glands in sialo cone-beam CT images.

PURPOSE: This study addressed the challenge of detecting and classifying the severity of ductopenia in parotid glands, a structural abnormality characterized by a reduced number of salivary ducts, previously shown to be associated with salivary gland impairment. The aim of the study was to develop an automatic algorithm designed to improve diagnostic accuracy and efficiency in analyzing ductopenic parotid glands using sialo cone-beam CT (sialo-CBCT) images. METHODS: We developed an end-to-end automatic pipeline consisting of three main steps: (1) region of interest (ROI) computation, (2) parotid gland segmentation using the Frangi filter, and (3) ductopenia case classification with a residual neural network (RNN) augmented by multidirectional maximum intensity projection (MIP) images. To explore the impact of the first two steps, the RNN was trained on three datasets: (1) original MIP images, (2) MIP images with predefined ROIs, and (3) MIP images after segmentation. RESULTS: Evaluation was conducted on 126 parotid sialo-CBCT scans of normal, moderate, and severe ductopenic cases, yielding a high performance of 100% for the ROI computation and 89% for the gland segmentation. Improvements in accuracy and F1 score were noted among the original MIP images (accuracy: 0.73, F1 score: 0.53), ROI-predefined images (accuracy: 0.78, F1 score: 0.56), and segmented images (accuracy: 0.95, F1 score: 0.90). Notably, ductopenic detection sensitivity was 0.99 in the segmented dataset, highlighting the capabilities of the algorithm in detecting ductopenic cases. CONCLUSIONS: Our method, which combines classical image processing and deep learning techniques, offers a promising solution for automatic detection of parotid glands ductopenia in sialo-CBCT scans. This may be used for further research aimed at understanding the role of presence and severity of ductopenia in salivary gland dysfunction.

Mandibular bone segmentation from CT scans: Quantitative and qualitative comparison among software.

OBJECTIVES: Nowadays, a wide variety of software for 3D reconstruction from CT scans is available; they differ for costs, capabilities, a priori knowledge, and, it is not trivial to identify the most suitable one for specific purposes. The article is aimed to provide some more information, having set up various metrics for the evaluation of different software's performance. METHODS: Metrics include software usability, segmentation quality, geometric accuracy, mesh properties and Dice Similarity Coefficient (DSC). Five different software have been considered (Mimics, D2P, Blue Sky Plan, Relu, and 3D Slicer) and tested on four cases; the mandibular bone was used as a benchmark. RESULTS: Relu software, being based on AI, was able to solve some very intricate geometry and proved to have a very good usability. On the other side, the time required for segmentation was significantly higher than other software (reaching over twice the time required by Mimics). Geometric distances between nodes position calculated by different software usually kept below 2.5 mm, reaching 3.1 mm in some very critical area; 75th percentile q75 is generally less than 0.5 mm, with a maximum of 1.11 mm. Dealing with consistency among software, the maximum DSC value was observed between Mimics and Slicer, D2P and Mimics, and D2P and Slicer, reaching 0.96. SIGNIFICANCE: This work has demonstrated how mandible segmentation performance among software was generally very good. Nonetheless, differences in geometric accuracy, usability, costs and times required can be significant so that information here provided can be useful to perform an informed choice.

SCANPatient: study protocol for a multi-centre, batched, stepped wedge, comparative effectiveness, randomised clinical trial of synoptic reporting of computerised tomography (CT) scans assessing cancers of the pancreas.

BACKGROUND: Complete surgical removal of pancreatic ductal adenocarcinoma (PDAC) is central to all curative treatment approaches for this aggressive disease, yet this is only possible in patients technically amenable to resection. Hence, an accurate assessment of whether patients are suitable for surgery is of paramount importance. The SCANPatient trial aims to test whether implementing a structured synoptic radiological report results in increased institutional accuracy in defining surgical resectability of non-metastatic PDAC. METHODS: SCANPatient is a batched, stepped wedge, comparative effectiveness, cluster randomised clinical trial. The trial will be conducted at 33 Australian hospitals all of which hold regular multi-disciplinary team meetings (MDMs) to discuss newly diagnosed patients with PDAC. Each site is required to manage a minimum of 20 patients per year (across all stages). Hospitals will be randomised to begin synoptic reporting within a batched, stepped wedge design. Initially all hospitals will continue to use their current reporting method; within each batch, after each 6-month period, a randomly selected group of hospitals will commence using the synoptic reports, until all hospitals are using synoptic reporting. Each hospital will provide data from patients who (i) are aged 18 or older; (ii) have suspected PDAC and have an abdominal CT scan, and (iii) are presented at a participating MDM. Non-metastatic patients will be documented as one of the following categories: (1) locally advanced and surgically unresectable; (2) borderline resectable; or (3) anatomically clearly resectable (Note: Metastatic disease is treated as a separate category). Data collection will last for 36 months in each batch, and a total of 2400 patients will be included. DISCUSSION: Better classifying patients with non-metastatic PDAC as having tumours that are either clearly resectable, borderline or locally advanced and unresectable may improve patient outcomes by optimising care and treatment planning. The borderline resectable group are a small but important cohort in whom surgery with curative intent may be considered; however, inconsistencies with definitions and an understanding of resectability status means these patients are often incorrectly classified and hence overlooked for curative options. TRIAL REGISTRATION: The SCANPatient trial was registered on 17th May 2023 in the Australian New Zealand Clinical Trials Registry (ANZCTR) (ACTRN12623000508673).

Improving assessment of lesions in longitudinal CT scans: a bi-institutional reader study on an AI-assisted registration and volumetric segmentation workflow.

PURPOSE: AI-assisted techniques for lesion registration and segmentation have the potential to make CT-based tumor follow-up assessment faster and less reader-dependent. However, empirical evidence on the advantages of AI-assisted volumetric segmentation for lymph node and soft tissue metastases in follow-up CT scans is lacking. The aim of this study was to assess the efficiency, quality, and inter-reader variability of an AI-assisted workflow for volumetric segmentation of lymph node and soft tissue metastases in follow-up CT scans. Three hypotheses were tested: (H1) Assessment time for follow-up lesion segmentation is reduced using an AI-assisted workflow. (H2) The quality of the AI-assisted segmentation is non-inferior to the quality of fully manual segmentation. (H3) The inter-reader variability of the resulting segmentations is reduced with AI assistance. MATERIALS AND METHODS: The study retrospectively analyzed 126 lymph nodes and 135 soft tissue metastases from 55 patients with stage IV melanoma. Three radiologists from two institutions performed both AI-assisted and manual segmentation, and the results were statistically analyzed and compared to a manual segmentation reference standard. RESULTS: AI-assisted segmentation reduced user interaction time significantly by 33% (222 s vs. 336 s), achieved similar Dice scores (0.80-0.84 vs. 0.81-0.82) and decreased inter-reader variability (median Dice 0.85-1.0 vs. 0.80-0.82; ICC 0.84 vs. 0.80), compared to manual segmentation. CONCLUSION: The findings of this study support the use of AI-assisted registration and volumetric segmentation for lymph node and soft tissue metastases in follow-up CT scans. The AI-assisted workflow achieved significant time savings, similar segmentation quality, and reduced inter-reader variability compared to manual segmentation.

Optimizing CT Abdomen-Pelvis Scan Radiation Dose: Examining the Role of Body Metrics (Waist Circumference, Hip Circumference, Abdominal Fat, and Body Mass Index) in Dose Efficiency.

Objective: This study investigates the correlation between patient body metrics and radiation dose in abdominopelvic CT scans, aiming to identify significant predictors of radiation exposure. Methods: Employing a cross-sectional analysis of patient data, including BMI, abdominal fat, waist, abdomen, and hip circumference, we analyzed their relationship with the following dose metrics: the CTDIvol, DLP, and SSDE. Results: Results from the analysis of various body measurements revealed that BMI, abdominal fat, and waist circumference are strongly correlated with increased radiation doses. Notably, the SSDE, as a more patient-centric dose metric, showed significant positive correlations, especially with waist circumference, suggesting its potential as a key predictor for optimizing radiation doses. Conclusions: The findings suggest that incorporating patient-specific body metrics into CT dosimetry could enhance personalized care and radiation safety. Conclusively, this study highlights the necessity for tailored imaging protocols based on individual body metrics to optimize radiation exposure, encouraging further research into predictive models and the integration of these metrics into clinical practice for improved patient management.

Optimizing double-layered convolutional neural networks for efficient lung cancer classification through hyperparameter optimization and advanced image pre-processing techniques.

Lung cancer remains a leading cause of cancer-related mortality globally, with prognosis significantly dependent on early-stage detection. Traditional diagnostic methods, though effective, often face challenges regarding accuracy, early detection, and scalability, being invasive, time-consuming, and prone to ambiguous interpretations. This study proposes an advanced machine learning model designed to enhance lung cancer stage classification using CT scan images, aiming to overcome these limitations by offering a faster, non-invasive, and reliable diagnostic tool. Utilizing the IQ-OTHNCCD lung cancer dataset, comprising CT scans from various stages of lung cancer and healthy individuals, we performed extensive preprocessing including resizing, normalization, and Gaussian blurring. A Convolutional Neural Network (CNN) was then trained on this preprocessed data, and class imbalance was addressed using Synthetic Minority Over-sampling Technique (SMOTE). The model's performance was evaluated through metrics such as accuracy, precision, recall, F1-score, and ROC curve analysis. The results demonstrated a classification accuracy of 99.64%, with precision, recall, and F1-score values exceeding 98% across all categories. SMOTE significantly enhanced the model's ability to classify underrepresented classes, contributing to the robustness of the diagnostic tool. These findings underscore the potential of machine learning in transforming lung cancer diagnostics, providing high accuracy in stage classification, which could facilitate early detection and tailored treatment strategies, ultimately improving patient outcomes.

The Diverse Utility of Contrast Media Delivery and Dosing During Computed Tomography: An International Assessment of Knowledge and Practices.

BACKGROUND: Despite the escalated production rate, the Iodinated Contrast Media (ICM) shortage continues, and demand outweighs supply. AIM: The aim of this study is to investigate the knowledge and practice of ICM delivery in computed tomography (CT) among radiographers and radiologic technologists worldwide. METHODS: An IRB-approved cross-sectional survey used Google Forms for data collection. It involved 94 CT radiographers from 27 countries and was divided into five sections. The first section gathered demographic information, followed by sections on experience, self-assessment of ICM reactions, and delivery technique. The third section explored ICM knowledge and its relation to CT parameters. The fourth and fifth sections focus on practices during pulmonary angiography CT and renal CT scans. Data analysis involved descriptive statistics, the Chi- Square test, and ANOVA. RESULTS: Knowledge was assessed with seven questions, and a score of at least 3.5 was needed for categorization. The median score was two, indicating low knowledge. Specifically, 64.9% of the participants scored lower than the two scores. Years of experience are strongly correlated with the level of knowledge, with 51.6% of radiographers having more than 10 years of experience demonstrating adequate knowledge. 41.7% of respondents demonstrated adequate knowledge when their duty was focused on CT. Furthermore, wide practice variability exists in all CT pulmonary angiography protocols among radiographers with adequate and inadequate knowledge. CONCLUSION: Inexperienced individuals showed knowledge gaps, leading to varied practices and highlighting the need for educational programs. The study underscores establishing standardized Protocols and Practice Guidelines (PPGs) for contrast media administration in Radiology Departments. Additionally, it emphasizes the importance of regular training programs, and international knowledge sharing. The potential for self-selection bias in the online survey sample is highlighted.

An Efficient CNN-Based Method for Intracranial Hemorrhage Segmentation from Computerized Tomography Imaging.

Intracranial hemorrhage (ICH) resulting from traumatic brain injury is a serious issue, often leading to death or long-term disability if not promptly diagnosed. Currently, doctors primarily use Computerized Tomography (CT) scans to detect and precisely locate a hemorrhage, typically interpreted by radiologists. However, this diagnostic process heavily relies on the expertise of medical professionals. To address potential errors, computer-aided diagnosis systems have been developed. In this study, we propose a new method that enhances the localization and segmentation of ICH lesions in CT scans by using multiple images created through different data augmentation techniques. We integrate residual connections into a U-Net-based segmentation network to improve the training efficiency. Our experiments, based on 82 CT scans from traumatic brain injury patients, validate the effectiveness of our approach, achieving an IOU score of 0.807 ± 0.03 for ICH segmentation using 10-fold cross-validation.