Impact of deep learning image reconstruction on volumetric accuracy and image quality of pulmonary nodules with different morphologies in low-dose CT.

BACKGROUND: This study systematically compares the impact of innovative deep learning image reconstruction (DLIR, TrueFidelity) to conventionally used iterative reconstruction (IR) on nodule volumetry and subjective image quality (IQ) at highly reduced radiation doses. This is essential in the context of low-dose CT lung cancer screening where accurate volumetry and characterization of pulmonary nodules in repeated CT scanning are indispensable. MATERIALS AND METHODS: A standardized CT dataset was established using an anthropomorphic chest phantom (Lungman, Kyoto Kaguku Inc., Kyoto, Japan) containing a set of 3D-printed lung nodules including six diameters (4 to 9 mm) and three morphology classes (lobular, spiculated, smooth), with an established ground truth. Images were acquired at varying radiation doses (6.04, 3.03, 1.54, 0.77, 0.41 and 0.20 mGy) and reconstructed with combinations of reconstruction kernels (soft and hard kernel) and reconstruction algorithms (ASIR-V and DLIR at low, medium and high strength). Semi-automatic volumetry measurements and subjective image quality scores recorded by five radiologists were analyzed with multiple linear regression and mixed-effect ordinal logistic regression models. RESULTS: Volumetric errors of nodules imaged with DLIR are up to 50% lower compared to ASIR-V, especially at radiation doses below 1 mGy and when reconstructed with a hard kernel. Also, across all nodule diameters and morphologies, volumetric errors are commonly lower with DLIR. Furthermore, DLIR renders higher subjective IQ, especially at the sub-mGy doses. Radiologists were up to nine times more likely to score the highest IQ-score to these images compared to those reconstructed with ASIR-V. Lung nodules with irregular margins and small diameters also had an increased likelihood (up to five times more likely) to be ascribed the best IQ scores when reconstructed with DLIR. CONCLUSION: We observed that DLIR performs as good as or even outperforms conventionally used reconstruction algorithms in terms of volumetric accuracy and subjective IQ of nodules in an anthropomorphic chest phantom. As such, DLIR potentially allows to lower the radiation dose to participants of lung cancer screening without compromising accurate measurement and characterization of lung nodules.

Influence of deep learning image reconstruction algorithm for reducing radiation dose and image noise compared to iterative reconstruction and filtered back projection for head and chest computed tomography examinations: a systematic review.

Background: The most recent advances in Computed Tomography (CT) image reconstruction technology are Deep learning image reconstruction (DLIR) algorithms. Due to drawbacks in Iterative reconstruction (IR) techniques such as negative image texture and nonlinear spatial resolutions, DLIRs are gradually replacing them. However, the potential use of DLIR in Head and Chest CT has to be examined further. Hence, the purpose of the study is to review the influence of DLIR on Radiation dose (RD), Image noise (IN), and outcomes of the studies compared with IR and FBP in Head and Chest CT examinations. Methods: We performed a detailed search in PubMed, Scopus, Web of Science, Cochrane Library, and Embase to find the articles reported using DLIR for Head and Chest CT examinations between 2017 to 2023. Data were retrieved from the short-listed studies using Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Results: Out of 196 articles searched, 15 articles were included. A total of 1292 sample size was included. 14 articles were rated as high and 1 article as moderate quality. All studies compared DLIR to IR techniques. 5 studies compared DLIR with IR and FBP. The review showed that DLIR improved IQ, and reduced RD and IN for CT Head and Chest examinations. Conclusions: DLIR algorithm have demonstrated a noted enhancement in IQ with reduced IN for CT Head and Chest examinations at lower dose compared with IR and FBP. DLIR showed potential for enhancing patient care by reducing radiation risks and increasing diagnostic accuracy.

Clinical usability of 3D gradient-echo-based ultrashort echo time imaging: Is it enough to facilitate diagnostic decision in real-world practice?

BACKGROUND: With recent advances in magnetic resonance imaging (MRI) technology, the practical role of lung MRI is expanding despite the inherent challenges of the thorax. The purpose of our study was to evaluate the current status of the concurrent dephasing and excitation (CODE) ultrashort echo-time sequence and the T1-weighted volumetric interpolated breath-hold examination (VIBE) sequence in the evaluation of thoracic disease by comparing it with the gold standard computed tomography (CT). METHODS: Twenty-four patients with lung cancer and mediastinal masses underwent both CT and MRI including T1-weighted VIBE and CODE. For CODE images, data were acquired in free breathing and end-expiratory images were reconstructed using retrospective respiratory gating. All images were evaluated through qualitative and quantitative approaches regarding various anatomical structures and lesions (nodule, mediastinal mass, emphysema, reticulation, honeycombing, bronchiectasis, pleural plaque and lymphadenopathy) inside the thorax in terms of diagnostic performance in making specific decisions. RESULTS: Depiction of the lung parenchyma, mediastinal and pleural lesion was not significant different among the three modalities (p > 0.05). Intra-tumoral and peritumoral features of lung nodules were not significant different in the CT, VIBE or CODE images (p > 0.05). However, VIBE and CODE had significantly lower image quality and poorer depiction of airway, great vessels, and emphysema compared to CT (p < 0.05). Image quality of central airways and depiction of bronchi were significantly better in CODE than in VIBE (p < 0.001 and p = 0.005). In contrast, the depiction of the vasculature was better for VIBE than CODE images (p = 0.003). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were significant greater in VIBE than CODE except for SNRlung and SNRnodule (p < 0.05). CONCLUSIONS: Our study showed the potential of CODE and VIBE sequences in the evaluation of localized thoracic abnormalities including solid pulmonary nodules.

Computed tomography myocardial perfusion imaging to detect myocardial ischemia in patients with anxiety and obstructive coronary heart disease post-exposure to mental stressors.

This study aims to measure myocardial blood flow (MBF) using dynamic CT- myocardial perfusion imaging (CT-MPI) combined with mental stressors in patients with obstructive coronary artery disease (OCAD) and in patients with anxiety and no obstructive coronary artery disease (ANOCAD). A total of 30 patients with OCAD with 30 patients with ANOCAD were included in this analysis. Using the 17-segment model, the rest and stress phase MBF of major coronary arteries in participants were recorded respectively. Compared with ANOCAD patients, OCAD patients were more likely to have localized reduction of MBF (p < 0.05). For patients with ANOCAD, both global MBF and MBF of the main coronary arteries in the stress phase were lower than those in the rest phase (all p < 0.05), but there was no significant difference in MBF among the main coronary arteries in the rest or stress phase (p = 0.25, p = 0.15). For patients with OCAD, the MBF of the target area was lower than that of the non-target area in both the rest and stress phase, and the MBF of the target area in the stress phase was lower than that in the rest phase (all p < 0.05). However, there was no significant difference in MBF between the rest or stress phase in the non-target area (p = 0.73). Under mental stress, the decrease in MBF in ANOCAD patients was diffuse, while the decrease in MBF in OCAD patients was localized. Dynamic CT-MPI combined with mental stressors can be used to detect MBF changes in anxiety patients.

Automated segmentation and volume prediction in pediatric Wilms' tumor CT using nnu-net.

BACKGROUND: Radiologic volumetric evaluation of Wilms' tumor (WT) is an important indicator to guide treatment decisions. However, due to the heterogeneity of the tumors, radiologists have main-guard differences in diagnosis that can lead to misdiagnosis and poor treatment. The aim of this study was to explore whether CT-based outlining of WT foci can be automated using deep learning. METHODS: We included CT intravenous phase images of 105 patients with WT and double-blind outlining of lesions by two radiologists. Then, we trained an automatic segmentation model using nnUnet. The Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95) were used to assess the performance. Next, we optimized the automatic segmentation results based on the ratio of the three-dimensional diameter of the lesion to improve the performance of volumetric assessment. RESULTS: The DSC and HD95 was 0.83 ± 0.22 and 10.50 ± 8.98 mm. The absolute difference and percentage difference in tumor size was 72.27 ± 134.84 cm3 and 21.08% ± 30.46%. After optimization according to our method, it decreased to 40.22 ± 96.06 cm3 and 10.16% ± 9.70%. CONCLUSION: We introduce a novel method that enhances the accuracy of predicting WT volume by integrating AI automated outlining and 3D tumor diameters. This approach surpasses the accuracy of using AI outcomes alone and has the potential to enhance the clinical evaluation of pediatric patients with WT. By intertwining AI outcomes with clinical data, this method becomes more interpretive and offers promising applications beyond Wilms tumor, extending to other pediatric diseases.

A clinical-radiomics nomogram based on dual-layer spectral detector CT to predict cancer stage in pancreatic ductal adenocarcinoma.

BACKGROUND: This study aimed to evaluate the efficacy of radiomics signatures derived from polyenergetic images (PEIs) and virtual monoenergetic images (VMIs) obtained through dual-layer spectral detector CT (DLCT). Moreover, it sought to develop a clinical-radiomics nomogram based on DLCT for predicting cancer stage (early stage: stage I-II, advanced stage: stage III-IV) in pancreatic ductal adenocarcinoma (PDAC). METHODS: A total of 173 patients histopathologically diagnosed with PDAC and who underwent contrast-enhanced DLCT were enrolled in this study. Among them, 49 were in the early stage, and 124 were in the advanced stage. Patients were randomly categorized into training (n = 122) and test (n = 51) cohorts at a 7:3 ratio. Radiomics features were extracted from PEIs and 40-keV VMIs were reconstructed at both arterial and portal venous phases. Radiomics signatures were constructed based on both PEIs and 40-keV VMIs. A radiomics nomogram was developed by integrating the 40-keV VMI-based radiomics signature with selected clinical predictors. The performance of the nomogram was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curves analysis (DCA). RESULTS: The PEI-based radiomics signature demonstrated satisfactory diagnostic efficacy, with the areas under the ROC curves (AUCs) of 0.92 in both the training and test cohorts. The optimal radiomics signature was based on 40-keV VMIs, with AUCs of 0.96 and 0.94 in the training and test cohorts. The nomogram, which integrated a 40-keV VMI-based radiomics signature with two clinical parameters (tumour diameter and normalized iodine density at the portal venous phase), demonstrated promising calibration and discrimination in both the training and test cohorts (0.97 and 0.91, respectively). DCA indicated that the clinical-radiomics nomogram provided the most significant clinical benefit. CONCLUSIONS: The radiomics signature derived from 40-keV VMI and the clinical-radiomics nomogram based on DLCT both exhibited exceptional performance in distinguishing early from advanced stages in PDAC, aiding clinical decision-making for patients with this condition.

Successful bilateral lung transplantation in pulmonary alveolar microlithiasis: A case report and review of literature.

BACKGROUND: Pulmonary alveolar microlithiasis (PAM) is a rare autosomal recessive genetic disorder with approximately 1000 known cases worldwide, in which calcium phosphate microliths deposit in the alveolar air spaces. As of writing this report, no definitive conventional therapy exists, and many PAM cases may progress to severe respiratory failure and potential death. Bilateral lung transplantation (BLx) seems to be the most optimal solution; however, this procedure is challenging along with limited reports regarding the outcome in PAM. We report a case of PAM successfully treated with BLx for the first time in Iran. METHOD: We present the case of a 42-year-old female with a longstanding history of cough, not responding to conventional antitussive medication, who was diagnosed as a case of PAM following a hospitalization due to coughing, dyspnea on exertion, and hemoptysis. Despite treatment with corticosteroid and medical treatment, no improvement was achieved and she subsequently developed respiratory and right ventricular failure, with oxygen ventilation dependence. Eventually, she was scheduled for BLx. The operation was successful and during her 2-year follow-up, no recurrence or significant postoperative complications has been reported. CONCLUSION: This case presentation and literature review confirm the effectiveness of BLx as a promising treatment for PAM-diagnosed patients, improving both life expectancy and quality of life.

Assessment of pulmonary vascular anatomy: comparing augmented reality by holograms versus standard CT images/reconstructions using surgical findings as reference standard.

BACKGROUND: We compared computed tomography (CT) images and holograms (HG) to assess the number of arteries of the lung lobes undergoing lobectomy and assessed easiness in interpretation by radiologists and thoracic surgeons with both techniques. METHODS: Patients scheduled for lobectomy for lung cancer were prospectively included and underwent CT for staging. A patient-specific three-dimensional model was generated and visualized in an augmented reality setting. One radiologist and one thoracic surgeon evaluated CT images and holograms to count lobar arteries, having as reference standard the number of arteries recorded at surgery. The easiness of vessel identification was graded according to a Likert scale. Wilcoxon signed-rank test and κ statistics were used. RESULTS: Fifty-two patients were prospectively included. The two doctors detected the same number of arteries in 44/52 images (85%) and in 51/52 holograms (98%). The mean difference between the number of artery branches detected by surgery and CT images was 0.31 ± 0.98, whereas it was 0.09 ± 0.37 between surgery and HGs (p = 0.433). In particular, the mean difference in the number of arteries detected in the upper lobes was 0.67 ± 1.08 between surgery and CT images and 0.17 ± 0.46 between surgery and holograms (p = 0.029). Both radiologist and surgeon showed a higher agreement for holograms (κ = 0.99) than for CT (κ = 0.81) and found holograms easier to evaluate than CTs (p < 0.001). CONCLUSIONS: Augmented reality by holograms is an effective tool for preoperative vascular anatomy assessment of lungs, especially when evaluating the upper lobes, more prone to anatomical variations. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04227444 RELEVANCE STATEMENT: Preoperative evaluation of the lung lobe arteries through augmented reality may help the thoracic surgeons to carefully plan a lobectomy, thus contributing to optimize patients' outcomes. KEY POINTS: • Preoperative assessment of the lung arteries may help surgical planning. • Lung artery detection by augmented reality was more accurate than that by CT images, particularly for the upper lobes. • The assessment of the lung arterial vessels was easier by using holograms than CT images.

A three-dimensional algorithm for precise measurement of human auricle parameters.

Measurement of auricle parameters for planning and post-operative evaluation presents substantial challenges due to the complex 3D structure of the human auricle. Traditional measurement methods rely on manual techniques, resulting in limited precision. This study introduces a novel automated surface-based three-dimensional measurement method for quantifying human auricle parameters. The method was applied to virtual auricles reconstructed from Computed Tomography (CT) scans of a cadaver head and subsequent measurement of important clinically relevant aesthetical auricular parameters (length, width, protrusion, position, auriculocephalic angle, and inclination angle). Reference measurements were done manually (using a caliper and using a 3D landmarking method) and measurement precision was compared to the automated method. The CT scans were performed using both a contemporary high-end and a low-end CT scanner. Scans were conducted at a standard scanning dose, and at half the dose. The automatic method demonstrated significantly higher precision in measuring auricle parameters compared to manual methods. Compared to traditional manual measurements, precision improved for auricle length (9×), width (5×), protrusion (5×), Auriculocephalic Angle (5-54×) and posteroanterior position (23×). Concerning parameters without comparison with a manual method, the precision level of supero-inferior position was 0.489 mm; and the precisions of the inclination angle measurements were 1.365 mm and 0.237 mm for the two automated methods investigated. Improved precision of measuring auricle parameters was associated with using the high-end scanner. A higher dose was only associated with a higher precision for the left auricle length. The findings of this study emphasize the advantage of automated surface-based auricle measurements, showcasing improved precision compared to traditional methods. This novel algorithm has the potential to enhance auricle reconstruction and other applications in plastic surgery, offering a promising avenue for future research and clinical application.

The prevalence of clinically relevant delayed intracranial hemorrhage in head trauma patients treated with oral anticoagulants is very low: a retrospective cohort register study.

BACKGROUND: Current guidelines from Scandinavian Neuro Committee mandate a 24-hour observation for head trauma patients on anticoagulants, even with normal initial head CT scans, as a means not to miss delayed intracranial hemorrhages. This study aimed to assess the prevalence, and time to diagnosis, of clinically relevant delayed intracranial hemorrhage in head trauma patients treated with oral anticoagulants. METHOD: Utilizing comprehensive two-year data from Region Skåne's emergency departments, which serve a population of 1.3 million inhabitants, this study focused on adult head trauma patients prescribed oral anticoagulants. We identified those with intracranial hemorrhage within 30 days, defining delayed intracranial hemorrhage as a bleeding not apparent on their initial CT head scan. These cases were further defined as clinically relevant if associated with mortality, any intensive care unit admission, or neurosurgery. RESULTS: Out of the included 2,362 head injury cases (median age 84, 56% on a direct acting oral anticoagulant), five developed delayed intracranial hemorrhages. None of these five cases underwent neurosurgery nor were admitted to an intensive care unit. Only two cases (0.08%, 95% confidence interval [0.01-0.3%]) were classified as clinically relevant, involving subdural hematomas in patients aged 82 and 87 years, who both subsequently died. The diagnosis of these delayed intracranial hemorrhages was made at 4 and 7 days following initial presentation to the emergency department. CONCLUSION: In patients with head trauma, on oral anticoagulation, the incidence of clinically relevant delayed intracranial hemorrhage was found to be less than one in a thousand, with detection occurring four days or later after initial presentation. This challenges the effectiveness of the 24-hour observation period recommended by the Scandinavian Neurotrauma Committee guidelines, suggesting a need to reassess these guidelines to optimise care and resource allocation. TRIAL REGISTRATION: This is a retrospective cohort study, does not include any intervention, and has therefore not been registered.

[VASCULAR OPHTHALMIC MANIFESTATIONS IN COVID-19 PATIENTS].

INTRODUCTION: Although COVID-19 is mainly a respiratory disease, recent evidence has emerged of vascular and procoagulant pathologies even in young and otherwise healthy individuals. Ophthalmic manifestations include, among others, visual impairment due to arteritic and venous retinal obstructions, which at times precedes other aspects of the disease. We present two atypical cases of internal carotid dissection (ICAD) and review the different ocular symptoms of ICAD and its association with the COVID-19 pandemic. BACKGROUND: A 43-year-old otherwise healthy man was referred to the Emergency Department with a headache and monocular blurring of vision. A recent fever (2 weeks prior) was noted on anamnesis, in light of absence of available positive PCR test during the illness period, clinical suspicion of COVID-19 was assumed. An initial ophthalmic evaluation found a mild optic nerve function impairment with preserved visual acuity. Computed tomography (CT) showed sinusitis, and an initial diagnosis was made of mild optic neuropathy secondary to sphenoid sinusitis. A few hours after admission, the patient reported deterioration of symptoms and examination revealed no light perception in his right eye and pale edematous optic nerve. Urgent magnetic resonance angiography (MRA) demonstrated right ICAD with no additional findings. The second patient, a 43-year-old man developed an acute event of strabismus, left limb paralysis, and speech difficulties while on a hospital visit for his son. The patient underwent CT of the brain which demonstrated extensive infarction following the distribution of his right cerebral artery. Continued investigation using computed tomography angiography (CTA) demonstrated a dissection of the right internal carotid artery. The patient was positive for COVID-19. DISCUSSION: In this review, we discuss 2 cases of carotid artery dissection presenting with an acute ocular complaint in two otherwise healthy young individuals. Events were suspected to have been provoked by COVID-19 infection. The pathogenesis and mechanisms behind COVID-19 induced coagulopathy are not clear, and several mechanisms have been proposed including endothelial damage and dysfunction. The virus is thought to enter endothelial cells and lead to a pathological procoagulant state. Awareness should be drawn to uncommon signs especially in young adults. Clotting issues can arise and should be treated quickly as they might be life and vision threatening.

Measurement of distances and locations of thoracic and lumbar vertebral bodies from CT scans in cases of spinal deformation.

BACKGROUND: Spinal deformations, except for acute injuries, are among the most frequent reasons for visiting an orthopaedic specialist and musculoskeletal treatment in adults and adolescents. Data on the morphology and anatomical structures of the spine are therefore of interest to orthopaedics, physicians, and medical scientists alike, in the broad field from diagnosis to therapy and in research. METHODS: Along the course of developing supplementary methods that do not require the use of ionizing radiation in the assessment of scoliosis, twenty CT scans from females and males with various severity of spinal deformations and body shape have been analysed with respect to the transverse distances between the vertebral body and the spinous process end tip and the skin, respectively, at thoracic and lumbar vertebral levels. Further, the locations of the vertebral bodies have been analysed in relation to the patient's individual body shape and shown together with those from other patients by normalization to the area encompassed by the transverse body contour. RESULTS: While the transverse distance from the vertebral body to the skin varies between patients, the distances from the vertebral body to the spinous processes end tips tend to be rather similar across different patients of the same gender. Tables list the arithmetic mean distances for all thoracic and lumbar vertebral levels and for different regions upon grouping into mild, medium, and strong spinal deformation and according to the range of spinal deformation. CONCLUSIONS: The distances, the clustering of the locations of the vertebral bodies as a function of the vertebral level, and the trends therein could in the future be used in context with biomechanical modeling of a patient's individual spinal deformation in scoliosis assessment using 3D body scanner images during follow-up examinations.

Correlating Reiff scores with clinical, functional, and prognostic factors: characterizing noncystic fibrosis bronchiectasis severity: validation from a nationwide multicenter study in Taiwan.

BACKGROUND: Our study aimed to confirm a simplified radiological scoring system, derived from a modified Reiff score, to evaluate its relationship with clinical symptoms and predictive outcomes in Taiwanese patients with noncystic fibrosis bronchiectasis (NCFB). METHODS: This extensive multicenter retrospective study, performed in Taiwan, concentrated on patients diagnosed with NCFB verified through high-resolution computed tomography (HRCT) scans. We not only compared the clinical features of various types of bronchiectasis (cylindrical, varicose, and cystic). Furthermore, we established relationships between the severity of clinical factors, including symptom scores, pulmonary function, pseudomonas aeruginosa colonization, exacerbation and admission rates, and HRCT parameters using modified Reiff scores. RESULTS: Data from 2,753 patients were classified based on HRCT patterns (cylindrical, varicose, and cystic) and severity, assessed by modified Reiff scores (mild, moderate, and severe). With increasing HRCT severity, a significant correlation was found with decreased forced expiratory volume in the first second (FEV1) (p < 0.001), heightened clinical symptoms (p < 0.001), elevated pathogen colonization (pseudomonas aeruginosa) (p < 0.001), and an increased annual hospitalization rate (p < 0.001). In the following multivariate analysis, elderly age, pseudomonas aeruginosa pneumonia, and hospitalizations per year emerged as the only independent predictors of mortality. CONCLUSION: Based on our large cohort study, the simplified CT scoring system (Reiff score) can serve as a useful adjunct to clinical factors in predicting disease severity and prognosis among Taiwanese patients with NCFB.

Assessment of myosteatosis and functionality in pretreatment gynecological cancer patients.

PURPOSE: We aimed to investigate the relationship between pretreatment gynecologic cancer survival and the physical function of patients with myosteatosis. Understanding this relationship prior to treatment would help healthcare providers identify and refer patients with poor muscle quality to an exercise program prior to treatment. METHODS: We conducted a cross-sectional analysis of 73 GC patients. Physical function was quantified using handgrip strength and an adapted version of the Senior Fitness Test (aerobic endurance not included). The EORTC QLC-C30 was used to evaluate general health quality. Myosteatosis (values below the median muscle radiodensity), muscle mass, and adipose tissue variables were calculated from the computed tomography (CT) scan at the third lumbar vertebra using specific software. RESULTS: Seventy patients (50.9 ± 15.2) were included; 41.5% had stage III or IV disease, and 61.4% had cervical cancer. The myosteatosis group was 11.9 years older and showed reduced functioning compared to the normal-radiodensity group. Age and Timed Up and Go (TUG) test results were shown to be the most reliable predictors of muscle radiodensity in pretreatment gynecological patients according to multivariate regression analysis (R2 = 0.314). CONCLUSION: Gynecological healthcare professionals should be aware that prompt exercise programs might be especially beneficial for older patients with reduced TUG performance to preserve muscle function and quality.

A novel model of artificial intelligence based automated image analysis of CT urography to identify bladder cancer in patients investigated for macroscopic hematuria.

OBJECTIVE: To evaluate whether artificial intelligence (AI) based automatic image analysis utilising convolutional neural networks (CNNs) can be used to evaluate computed tomography urography (CTU) for the presence of urinary bladder cancer (UBC) in patients with macroscopic hematuria. METHODS: Our study included patients who had undergone evaluation for macroscopic hematuria. A CNN-based AI model was trained and validated on the CTUs included in the study on a dedicated research platform (Recomia.org). Sensitivity and specificity were calculated to assess the performance of the AI model. Cystoscopy findings were used as the reference method. RESULTS: The training cohort comprised a total of 530 patients. Following the optimisation process, we developed the last version of our AI model. Subsequently, we utilised the model in the validation cohort which included an additional 400 patients (including 239 patients with UBC). The AI model had a sensitivity of 0.83 (95% confidence intervals [CI], 0.76-0.89), specificity of 0.76 (95% CI 0.67-0.84), and a negative predictive value (NPV) of 0.97 (95% CI 0.95-0.98). The majority of tumours in the false negative group (n = 24) were solitary (67%) and smaller than 1 cm (50%), with the majority of patients having cTaG1-2 (71%). CONCLUSIONS: We developed and tested an AI model for automatic image analysis of CTUs to detect UBC in patients with macroscopic hematuria. This model showed promising results with a high detection rate and excessive NPV. Further developments could lead to a decreased need for invasive investigations and prioritising patients with serious tumours.

Diagnostic accuracy of contrast-enhanced computed tomography in assessing bone invasion in patients with oral squamous cell carcinoma.

OBJECTIVES: This study aimed to evaluate the diagnostic accuracy of contrast-enhanced computed tomography (CT) in detecting bone invasion in oral squamous cell carcinoma (OSCC) patients and to explore clinicopathological factors associated with its reliability. MATERIALS AND METHODS: 417 patients underwent preoperative contrast-enhanced CT followed by radical surgery. The presence or absence of bone invasion served as the outcome variable, with histopathologic examination of the resection specimen considered the gold standard. Statistical analyses, comprising correlation analyses and the determination of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were conducted. RESULTS: CT exhibited 76.85% sensitivity, 82.20% specificity, 47.14% PPV, and 89.67% NPV. False-positive and false-negative rates were 11.27% and 5.99%, respectively. Artifacts affected assessment in 44 patients, but not in those with bone invasion. Tumor size, depth of invasion (DOI), tumor localization at the upper jaw, lymphatic invasion, and perineural invasion correlated with incorrect identification of bone invasion (Chi-square, p < 0.05). CONCLUSIONS: Despite utilizing thin-section CT, notable false-positive and false-negative results persisted. Patients with T3 tumors, DOI ≥ 10 mm, or upper jaw tumors are at higher risk for misidentification of bone invasion. Combining multiple methods may enhance diagnostic accuracy, and the integration of artificial intelligence or tracking electrolyte disturbances by tumor depth profiling shows promise for further assessment of bone invasion before histopathology. CLINICAL RELEVANCE: Surgeons should consider these insights when planning tumor resection. Supplementary imaging may be warranted in cases with high risk factors for misidentification. Further methodological advancements are crucial for enhancing diagnostic precision.

Deep Learning Assessment of Small Renal Masses at Contrast-enhanced Multiphase CT.

Background Accurate characterization of suspicious small renal masses is crucial for optimized management. Deep learning (DL) algorithms may assist with this effort. Purpose To develop and validate a DL algorithm for identifying benign small renal masses at contrast-enhanced multiphase CT. Materials and Methods Surgically resected renal masses measuring 3 cm or less in diameter at contrast-enhanced CT were included. The DL algorithm was developed by using retrospective data from one hospital between 2009 and 2021, with patients randomly allocated in a training and internal test set ratio of 8:2. Between 2013 and 2021, external testing was performed on data from five independent hospitals. A prospective test set was obtained between 2021 and 2022 from one hospital. Algorithm performance was evaluated by using the area under the receiver operating characteristic curve (AUC) and compared with the results of seven clinicians using the DeLong test. Results A total of 1703 patients (mean age, 56 years ± 12 [SD]; 619 female) with a single renal mass per patient were evaluated. The retrospective data set included 1063 lesions (874 in training set, 189 internal test set); the multicenter external test set included 537 lesions (12.3%, 66 benign) with 89 subcentimeter (≤1 cm) lesions (16.6%); and the prospective test set included 103 lesions (13.6%, 14 benign) with 20 (19.4%) subcentimeter lesions. The DL algorithm performance was comparable with that of urological radiologists: for the external test set, AUC was 0.80 (95% CI: 0.75, 0.85) versus 0.84 (95% CI: 0.78, 0.88) (P = .61); for the prospective test set, AUC was 0.87 (95% CI: 0.79, 0.93) versus 0.92 (95% CI: 0.86, 0.96) (P = .70). For subcentimeter lesions in the external test set, the algorithm and urological radiologists had similar AUC of 0.74 (95% CI: 0.63, 0.83) and 0.81 (95% CI: 0.68, 0.92) (P = .78), respectively. Conclusion The multiphase CT-based DL algorithm showed comparable performance with that of radiologists for identifying benign small renal masses, including lesions of 1 cm or less. Published under a CC BY 4.0 license. Supplemental material is available for this article.

Developing and validating multi-omics prediction models for late patient-reported dysphagia in head and neck radiotherapy.

Background and purpose. To investigate models developed using radiomic and dosiomic (multi-omics) features from planning and treatment imaging for late patient-reported dysphagia in head and neck radiotherapy.Materials and methods. Training (n = 64) and testing (n = 23) cohorts of head and neck cancer patients treated with curative intent chemo-radiotherapy with a follow-up time greater than 12 months were retrospectively examined. Patients completed the MD Anderson Dysphagia Inventory and a composite score ≤60 was interpreted as patient-reported dysphagia. A chart review collected baseline dysphagia and clinical factors. Multi-omic features were extracted from planning and last synthetic CT images using the pharyngeal constrictor muscle contours as a region of interest. Late patient-reported dysphagia models were developed using a random forest backbone, with feature selection and up-sampling methods to account for the imbalanced data. Models were developed and validated for multi-omic feature combinations for both timepoints.Results. A clinical and radiomic feature model developed using the planning CT achieved good performance (validation: sensitivity = 80 ± 27% / balanced accuracy = 71 ± 23%, testing: sensitivity = 80 ± 10% / balanced accuracy = 73 ± 11%). The synthetic CT models did not show improvement over the plan CT multi-omics models, with poor reliability of the radiomic features on these images. Dosiomic features extracted from the synthetic CT showed promise in predicting late patient-reported dysphagia.Conclusion. Multi-omics models can predict late patient-reported dysphagia in head and neck radiotherapy patients. Synthetic CT dosiomic features show promise in developing successful models to account for changes in delivered dose distribution. Multi-center or prospective studies are required prior to clinical implementation of these models.