Multimodal modeling with low-dose CT and clinical information for diagnostic artificial intelligence on mediastinal tumors: a preliminary study.

BACKGROUND: Diagnosing mediastinal tumours, including incidental lesions, using low-dose CT (LDCT) performed for lung cancer screening, is challenging. It often requires additional invasive and costly tests for proper characterisation and surgical planning. This indicates the need for a more efficient and patient-centred approach, suggesting a gap in the existing diagnostic methods and the potential for artificial intelligence technologies to address this gap. This study aimed to create a multimodal hybrid transformer model using the Vision Transformer that leverages LDCT features and clinical data to improve surgical decision-making for patients with incidentally detected mediastinal tumours. METHODS: This retrospective study analysed patients with mediastinal tumours between 2010 and 2021. Patients eligible for surgery (n=30) were considered 'positive,' whereas those without tumour enlargement (n=32) were considered 'negative.' We developed a hybrid model combining a convolutional neural network with a transformer to integrate imaging and clinical data. The dataset was split in a 5:3:2 ratio for training, validation and testing. The model's efficacy was evaluated using a receiver operating characteristic (ROC) analysis across 25 iterations of random assignments and compared against conventional radiomics models and models excluding clinical data. RESULTS: The multimodal hybrid model demonstrated a mean area under the curve (AUC) of 0.90, significantly outperforming the non-clinical data model (AUC=0.86, p=0.04) and radiomics models (random forest AUC=0.81, p=0.008; logistic regression AUC=0.77, p=0.004). CONCLUSION: Integrating clinical and LDCT data using a hybrid transformer model can improve surgical decision-making for mediastinal tumours, showing superiority over models lacking clinical data integration.

Deep learning model to predict Ki-67 expression of breast cancer using digital breast tomosynthesis.

BACKGROUND: Developing a deep learning (DL) model for digital breast tomosynthesis (DBT) images to predict Ki-67 expression. METHODS: The institutional review board approved this retrospective study and waived the requirement for informed consent from the patients. Initially, 499 patients (mean age: 50.5 years, range: 29-90 years) referred to our hospital for breast cancer were participated, 126 patients with pathologically confirmed breast cancer were selected and their Ki-67 expression measured. The Xception architecture was used in the DL model to predict Ki-67 expression levels. The high Ki-67 vs low Ki-67 expression diagnostic performance of our DL model was assessed by accuracy, sensitivity, specificity, areas under the receiver operating characteristic curve (AUC), and by using sub-datasets divided by the radiological characteristics of breast cancer. RESULTS: The average accuracy, sensitivity, specificity, and AUC were 0.912, 0.629, 0.985, and 0.883, respectively. The AUC of the four subgroups separated by radiological findings for the mass, calcification, distortion, and focal asymmetric density sub-datasets were 0.890, 0.750, 0.870, and 0.660, respectively. CONCLUSIONS: Our results suggest the potential application of our DL model to predict the expression of Ki-67 using DBT, which may be useful for preoperatively determining the treatment strategy for breast cancer.

Neuropil variation in the prefrontal, motor, and visual cortex of six felids.

Felids have evolved a specialized suite of morphological adaptations for obligate carnivory. Although the musculoskeletal anatomy of the Felidae has been studied extensively, the comparative neuroanatomy of felids is relatively unexplored. Little is known about how variation in cerebral anatomy of felids relates to species-specific differences in sociality, hunting strategy, or activity patterns. We quantitatively analyzed neuropil variation in the prefrontal, primary motor, and primary visual cortices of six species of Felidae (Panthera leo, Panthera uncia, Panthera tigris, Panthera Leopardus, Acinonyx jubatus, Felis sylvestris domesticus) to investigate relationships with brain size, neuronal cell parameters, and select behavioral and ecological factors. Neuropil is the dense, intricate network of axons, dendrites, and synapses in the brain, playing a critical role in information processing and communication between neurons. There were significant species and regional differences in neuropil proportions, with African lion, cheetah, and tiger having more neuropil in all three cortical regions in comparison to the other species. Based on regression analyses, we find that the increased neuropil fraction in the prefrontal cortex supports social and behavioral flexibility, while in the primary motor cortex this facilitates the neural activity needed for hunting movements. Greater neuropil fraction in the primary visual cortex may contribute to visual requirements associated with diel activity patterns. These results provide a cross species comparison of neuropil fraction variation in the Felidae, particularly the understudied Panthera, and provide evidence for convergence of the neuroanatomy of Panthera and cheetahs.

Computer-aided diagnosis of chest X-ray for COVID-19 diagnosis in external validation study by radiologists with and without deep learning system.

To evaluate the diagnostic performance of our deep learning (DL) model of COVID-19 and investigate whether the diagnostic performance of radiologists was improved by referring to our model. Our datasets contained chest X-rays (CXRs) for the following three categories: normal (NORMAL), non-COVID-19 pneumonia (PNEUMONIA), and COVID-19 pneumonia (COVID). We used two public datasets and private dataset collected from eight hospitals for the development and external validation of our DL model (26,393 CXRs). Eight radiologists performed two reading sessions: one session was performed with reference to CXRs only, and the other was performed with reference to both CXRs and the results of the DL model. The evaluation metrics for the reading session were accuracy, sensitivity, specificity, and area under the curve (AUC). The accuracy of our DL model was 0.733, and that of the eight radiologists without DL was 0.696 ± 0.031. There was a significant difference in AUC between the radiologists with and without DL for COVID versus NORMAL or PNEUMONIA (p = 0.0038). Our DL model alone showed better diagnostic performance than that of most radiologists. In addition, our model significantly improved the diagnostic performance of radiologists for COVID versus NORMAL or PNEUMONIA.

Deep learning model for predicting the presence of stromal invasion of breast cancer on digital breast tomosynthesis.

To develop a deep learning (DL)-based algorithm to predict the presence of stromal invasion in breast cancer using digital breast tomosynthesis (DBT). Our institutional review board approved this retrospective study and waived the requirement for informed consent from the patients. Initially, 499 patients (mean age 50.5 years, age range, 29-90 years) who were referred to our hospital under the suspicion of breast cancer and who underwent DBT between March 1 and August 31, 2019, were enrolled in this study. Among the 499 patients, 140 who underwent surgery after being diagnosed with breast cancer were selected for the analysis. Based on the pathological reports, the 140 patients were classified into two groups: those with non-invasive cancer (n = 20) and those with invasive cancer (n = 120). VGG16, Resnet50, DenseNet121, and Xception architectures were used as DL models to differentiate non-invasive from invasive cancer. The diagnostic performance of the DL models was assessed based on the area under the receiver operating characteristic curve (AUC). The AUC for the four models were 0.56 [95% confidence intervals (95% CI) 0.49-0.62], 0.67 (95% CI 0.62-0.74), 0.71 (95% CI 0.65-0.75), and 0.75 (95% CI 0.69-0.81), respectively. Our proposed DL model trained on DBT images is useful for predicting the presence of stromal invasion in breast cancer.

The location of unilateral axillary lymphadenopathy after COVID-19 vaccination compared with that of metastasis from breast cancer without vaccination.

PURPOSE: Unilateral axillary lymphadenopathy is known to occur after coronavirus disease (COVID-19) vaccination. Post-vaccination lymphadenopathy may mimic the metastatic lymph nodes in breast cancer, and it is challenging to distinguish between them. This study investigated whether the localization of axillary lymphadenopathy on magnetic resonance imaging (MRI) could be used to distinguish reactive lymphadenopathy after COVID-19 vaccines from metastatic nodes. MATERIALS AND METHODS: We retrospectively examined preoperative MRI images of 684 axillae in 342 patients who underwent breast cancer surgery from June to October 2021. Lymphadenopathy was defined as cortical thickening or short axis ≥ 5 mm. The axilla was divided into ventral and dorsal parts on the axial plane using a perpendicular line extending from the most anterior margin of the muscle group, including the deltoid, latissimus dorsi, or teres major muscles, relative to a line along the lateral chest wall. We recorded the presence or absence of axillary lymphadenopathy in each area and the number of visible lymph nodes. RESULTS: Of 80 axillae, 41 and 39 were included in the vaccine and metastasis groups, respectively. The median time from the last vaccination to MRI was 19 days in the vaccine group. The number of visible axillary lymph nodes was significantly higher in the vaccine group (median, 15 nodes) than in the metastasis group (7 nodes) (P < 0.001). Dorsal lymphadenopathy was observed in 16 (39.0%) and two (5.1%) axillae in the vaccine and metastasis groups, respectively (P < 0.001). If the presence of both ventral and dorsal lymphadenopathy is considered indicative of vaccine-induced reaction, this finding has a sensitivity of 34.1%, specificity of 97.4%, and positive and negative predictive values of 93.3％ and 58.5%, respectively. CONCLUSION: The presence of deep axillary lymphadenopathy may be an important factor for distinguishing post-vaccination lymphadenopathy from metastasis. The number of axillary lymph nodes may also help.

Why is the depth/width ratio of a typical fibroadenoma small in breast ultrasonography?

PURPOSE: Typical myxomatous fibroadenomas have a small depth/width (D/W) ratio on ultrasonography. The small D/W ratio of fibroadenomas is speculated to be caused by the softness of the mass and its orientation along the longitudinal aspect of the ductal elements without adhesion to the surrounding tissue; however, this has not been clearly proven. This study aimed to confirm the reason why fibroadenomas present with a small D/W ratio on ultrasonography. METHODS: We retrospectively analyzed imaging data from 17 patients who were diagnosed with typical fibroadenomas on ultrasonography and who underwent magnetic resonance imaging (MRI) at our hospital. RESULTS: The median D/W ratio obtained from ultrasonography images was 0.48 (0.32-0.67), while that obtained from MRI was 1.38 (0.62-1.68). The D/W ratios calculated from MRI were significantly greater than those calculated from ultrasonography images (p < 0.001). The D/W ratio obtained using ultrasonography was not greater than the D/W ratio obtained using MRI in any of the cases. CONCLUSION: This study revealed that the small D/W ratio of fibroadenomas on ultrasonography may be attributable to the horizontal force acting on the breast against the chest wall in the supine position, the elasticity of the fibroadenoma, and the lack of adhesion between the mass and surrounding tissue.

Deep learning model for breast cancer diagnosis based on bilateral asymmetrical detection (BilAD) in digital breast tomosynthesis images.

The purpose of this study was to develop a deep learning model to diagnose breast cancer by embedding a diagnostic algorithm that examines the asymmetry of bilateral breast tissue. This retrospective study was approved by the institutional review board. A total of 115 patients who underwent breast surgery and had pathologically confirmed breast cancer were enrolled in this study. Two image pairs [230 pairs of bilateral breast digital breast tomosynthesis (DBT) images with 115 malignant tumors and contralateral tissue (M/N), and 115 bilateral normal areas (N/N)] were generated from each patient enrolled in this study. The proposed deep learning model is called bilateral asymmetrical detection (BilAD), which is a modified convolutional neural network (CNN) model of Xception with two-dimensional tensors for bilateral breast images. BilAD was trained to classify the differences between pairs of M/N and N/N datasets. The results of the BilAD model were compared to those of the unilateral control CNN model (uCNN). The results of BilAD and the uCNN were as follows: accuracy, 0.84 and 0.75; sensitivity, 0.73 and 0.58; and specificity, 0.93 and 0.92, respectively. The mean area under the receiver operating characteristic curve of BilAD was significantly higher than that of the uCNN (p = 0.02): 0.90 and 0.84, respectively. The proposed deep learning model trained by embedding a diagnostic algorithm to examine the asymmetry of bilateral breast tissue improves the diagnostic accuracy for breast cancer.

Improvement in metabolic indices including thyroid hormones via enhanced absorption of nutrients by Teduglutide in short bowel syndrome.

INTRODUCTION AND IMPORTANCE: Short bowel syndrome is characterized by maldigestion and malabsorption resulting in deficiencies of multiple nutrients including vitamins and minerals. Most subjects required parental elimination for survival. GLP-2 RA Teduglutide was recently approved for treatment of short bowel syndrome especially for those requiring parenteral support. Our intent in reporting this subject is to demonstrate the utility of Teduglutide in improving multiple metabolic indices in presence of short bowel syndrome. CASE PRESENTATION AND CLINICAL DISCUSSION: 66-year-old Caucasian female presented with a history of short bowel syndrome and associated vitamin deficiencies, hypothyroidism requiring large dose (300 µg) of levothyroxine, diarrhea and liver cirrhosis. Upon starting teduglutide the subject saw improvement in her symptoms. Moreover, daily dose of Levothyroxine required a gradual decrease to maintain desirable serum concentrations of Free T4, Free T3 and TSH. Serum levels of several vitamins attained greater than therapeutic concentrations requiring dosage reductions. Also notable was the improvement in her liver function tests, remission from ascites and episodes of hepatic encephalopathy and regeneration of liver nodules. CONCLUSION: Following administration of GLP2 therapy, an adult subject with short bowel syndrome with concurrent hypothyroidism and multiple vitamin deficiencies, demonstrated a marked improvement in her metabolic parameters resulting in reduction in daily medication doses along with improvement in manifestations of liver cirrhosis.

Assessment of hepatic prostaglandin E2 level in carbamazepine induced liver injury.

Objective. Carbamazepine (CBZ), a widely used antiepileptic drug, is one major cause of the idiosyncratic liver injury along with immune reactions. Conversely, prostaglandin E2 (PGE2) demonstrates a hepatoprotective effect by regulating immune reactions and promoting liver repair in various types of liver injury. However, the amount of hepatic PGE2 during CBZ-induced liver injury remains elusive. In this study, we aimed to evaluate the hepatic PGE2 levels during CBZ-induced liver injury using a mouse model. Methods. Mice were orally administered with CBZ at a dose of 400 mg/kg for 4 days, and 800 mg/kg on the 5th day. Results. Plasma alanine transaminase (ALT) level increased in some of mice 24 h after the last CBZ administration. Although median value of hepatic PGE2 amount in the CBZ-treated mice showed same extent as vehicle-treated control mice, it exhibited significant elevated level in mice with severe liver injury presented by a plasma ALT level >1000 IU/L. According to these results, mice had a plasma ALT level >1000 IU/L were defined as responders and the others as non-responders in this study. Even though, the hepatic PGE2 levels increased in responders, the hepatic expression and enzyme activity related to PGE2 production were not upregulated when compared with vehicle-treated control mice. However, the hepatic 15-hydroxyprostaglandin dehydrogenase (15-PGDH) expression and activity decreased significantly in responders when compared with control mice. Conclusions. These results indicate that elevated hepatic PGE2 levels can be attributed to the downregulation of 15-PGDH expression under CBZ-induced liver injury.

Left omental artery bleeding in two patients with segmental arterial mediolysis successfully isolated with coil embolization.

BACKGROUND: Segmental arterial mediolysis (SAM) is a rare, nonatherosclerotic, noninflammatory arteriopathy of unknown etiology, rarely involving omental artery (OA). No case reports have described left OA bleeding successfully treated with transarterial embolization (TAE) with coils. This report describes two cases of SAM-affected left OA bleeding successfully embolized using isolation technique with coils, recognizing the potential for the greater omentum to have arterial collateral network between OAs. CASE PRESENTATION: Case 1. A 55-year-old male with no significant past medical history presented with an acute abdomen. Contrast-enhanced computed tomography (CT) revealed possible hemorrhagic ascites involving the left portion of the greater omentum and dilated, stenotic change of the left OA with a possible hematoma. SAM-associated left OA bleeding was suspected. Given its acute-angled branching from a splenic artery or branch and long, tortuous catheter-trajectory, we used a triaxial catheter system. Left OA angiography revealed the proximal dilated, stenotic change and a distal pseudoaneurysm. Isolation was successfully performed with coils. Because he had no abdominal pain or progressive anemia, he was discharged on hospital day 5. Neither recurrence nor new SAM-associated findings were observed during two-years of follow-up. Case 2. A 60-year-old-man with no significant past medical history presented with an acute abdomen. CT revealed similar finding as Case 1. SAM-associated left OA bleeding was suspected. Left OA angiography revealed proximal dilated, stenotic change with distal occlusion. Despite having no signs of active bleeding, review of the CT and angiography findings suggested the left OA as the bleeding site. Given proximal embolization at this point could lead to incomplete hemostasis or rebleeding via the arterial collateral network between OAs, an attempt was made to navigate the microcatheter into the distal side beyond the occlusion. Distal left OA angiography confirmed that the distal OA over the occlusion was intact and directly communicated with a right OA arising from right gastroepiploic artery. The SAM-associated lesion was successfully isolated with coils. Because he had no abdominal pain or progressive anemia, he was transported to another hospital on hospital day 3. Neither recurrence nor new SAM-associated findings were observed during two-years follow-up. CONCLUSION: SAM can involve left OA and be controlled using an isolation technique with coils.

The regulatory mechanism involved in the prostaglandin E2 disposition in carbon tetrachloride-induced liver injury.

Prostaglandin E2 (PGE2) exhibits hepatoprotective effects against various types of liver injury. However, there is little information on the disposition of endogenous PGE2 during liver injury. In the present study, we attempted to elucidate the mechanism involved in regulating PGE2 distribution during liver injury. Carbon tetrachloride (CCl4) was used to establish a liver injury mouse model. PGE2 was measured by LC-MS/MS. The plasma and hepatic PGE2 levels were significantly increased at 6 to 48 h after CCl4 treatment. The ratio of plasma levels of 13,14-dihydro-15-ketoPGE2 (PGEM), a major PGE2 metabolite, to PGE2 decreased significantly after CCl4 treatment. PGE2 synthesis and expression of enzymes related to PGE2 production were not induced, while the activity and mRNA expression of 15-prostaglandin dehydrogenase (15-PGDH/Hpgd), a major enzyme for PGE2 inactivation, decreased significantly in the liver of CCl4-treated mice compared to that of vehicle-treated control. The plasma and hepatic PGE2 levels were negatively correlated with the hepatic mRNA expression levels of Hpgd. Although the mRNA expression of organic anion transporting polypeptide 2A1 (OATP2A1/Slco2a1), a major PGE2 transporter, was upregulated, other hepatic OATPs decreased significantly at 24 h after CCl4 treatment. Immunohistochemical analysis indicated that 15-PGDH was mainly expressed in endothelial cells and that OATP2A1 was expressed at least in endothelial cells and Kupffer cells in the liver. These results suggest that the decreased 15-PGDH expression in hepatic endothelial cells is the principal mechanism for the increase in hepatic and plasma PGE2 levels due to the CCl4-induced liver injury.