Deep Learning Detects Changes Indicative of Axial Spondyloarthritis at MRI of Sacroiliac Joints.

Background MRI is frequently used for early diagnosis of axial spondyloarthritis (axSpA). However, evaluation is time-consuming and requires profound expertise because noninflammatory degenerative changes can mimic axSpA, and early signs may therefore be missed. Deep neural networks could function as assistance for axSpA detection. Purpose To create a deep neural network to detect MRI changes in sacroiliac joints indicative of axSpA. Materials and Methods This retrospective multicenter study included MRI examinations of five cohorts of patients with clinical suspicion of axSpA collected at university and community hospitals between January 2006 and September 2020. Data from four cohorts were used as the training set, and data from one cohort as the external test set. Each MRI examination in the training and test sets was scored by six and seven raters, respectively, for inflammatory changes (bone marrow edema, enthesitis) and structural changes (erosions, sclerosis). A deep learning tool to detect changes indicative of axSpA was developed. First, a neural network to homogenize the images, then a classification network were trained. Performance was evaluated with use of area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. P < .05 was considered indicative of statistically significant difference. Results Overall, 593 patients (mean age, 37 years ± 11 [SD]; 302 women) were studied. Inflammatory and structural changes were found in 197 of 477 patients (41%) and 244 of 477 (51%), respectively, in the training set and 25 of 116 patients (22%) and 26 of 116 (22%) in the test set. The AUCs were 0.94 (95% CI: 0.84, 0.97) for all inflammatory changes, 0.88 (95% CI: 0.80, 0.95) for inflammatory changes fulfilling the Assessment of SpondyloArthritis international Society definition, and 0.89 (95% CI: 0.81, 0.96) for structural changes indicative of axSpA. Sensitivity and specificity on the external test set were 22 of 25 patients (88%) and 65 of 91 patients (71%), respectively, for inflammatory changes and 22 of 26 patients (85%) and 70 of 90 patients (78%) for structural changes. Conclusion Deep neural networks can detect inflammatory or structural changes to the sacroiliac joint indicative of axial spondyloarthritis at MRI. © RSNA, 2022 Online supplemental material is available for this article.

Highly accurate classification of chest radiographic reports using a deep learning natural language model pre-trained on 3.8 million text reports.

MOTIVATION: The development of deep, bidirectional transformers such as Bidirectional Encoder Representations from Transformers (BERT) led to an outperformance of several Natural Language Processing (NLP) benchmarks. Especially in radiology, large amounts of free-text data are generated in daily clinical workflow. These report texts could be of particular use for the generation of labels in machine learning, especially for image classification. However, as report texts are mostly unstructured, advanced NLP methods are needed to enable accurate text classification. While neural networks can be used for this purpose, they must first be trained on large amounts of manually labelled data to achieve good results. In contrast, BERT models can be pre-trained on unlabelled data and then only require fine tuning on a small amount of manually labelled data to achieve even better results. RESULTS: Using BERT to identify the most important findings in intensive care chest radiograph reports, we achieve areas under the receiver operation characteristics curve of 0.98 for congestion, 0.97 for effusion, 0.97 for consolidation and 0.99 for pneumothorax, surpassing the accuracy of previous approaches with comparatively little annotation effort. Our approach could therefore help to improve information extraction from free-text medical reports. Availability and implementationWe make the source code for fine-tuning the BERT-models freely available at https://github.com/fast-raidiology/bert-for-radiology. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Evaluation of potential tissue heating during percutaneous drill-assisted bone sampling in an in vivo porcine study.

BACKGROUND: Minimally invasive, battery-powered drilling systems have become the preferred tool for obtaining representative samples from bone lesions. However, the heat generated during battery-powered bone drilling for bone biopsies has not yet been sufficiently investigated. Thermal necrosis can occur if the bone temperature exceeds a critical threshold for a certain period of time. PURPOSE: To investigate heat production as a function of femur temperature during and after battery-powered percutaneous bone drilling in a porcine in vivo model. METHODS: We performed 16 femur drillings in 13 domestic pigs with an average age of 22 weeks and an average body temperature of 39.7 °C, using a battery-powered drilling system and an intraosseous temperature monitoring device. The standardized duration of the drilling procedure was 20 s. The bone core specimens obtained were embedded in 4% formalin, stained with haematoxylin and eosin (H&E) and sent for pathological analysis of tissue quality and signs of thermal damage. RESULTS: No significant changes in the pigs' local temperature were observed after bone drilling with a battery-powered drill device. Across all measurements, the median change in temperature between the initial measurement and the temperature measured after drilling (at 20 s) was 0.1 °C. Histological examination of the bone core specimens revealed no signs of mechanical or thermal damage. CONCLUSION: Overall, this preliminary study shows that battery-powered, drill-assisted harvesting of bone core specimens does not appear to cause mechanical or thermal damage.

Deep learning for accurately recognizing common causes of shoulder pain on radiographs.

OBJECTIVE: Training a convolutional neural network (CNN) to detect the most common causes of shoulder pain on plain radiographs and to assess its potential value in serving as an assistive device to physicians. MATERIALS AND METHODS: We used a CNN of the ResNet-50 architecture which was trained on 2700 shoulder radiographs from clinical practice of multiple institutions. All radiographs were reviewed and labeled for six findings: proximal humeral fractures, joint dislocation, periarticular calcification, osteoarthritis, osteosynthesis, and joint endoprosthesis. The trained model was then evaluated on a separate test dataset, which was previously annotated by three independent expert radiologists. Both the training and the test datasets included radiographs of highly variable image quality to reflect the clinical situation and to foster robustness of the CNN. Performance of the model was evaluated using receiver operating characteristic (ROC) curves, the thereof derived AUC as well as sensitivity and specificity. RESULTS: The developed CNN demonstrated a high accuracy with an area under the curve (AUC) of 0.871 for detecting fractures, 0.896 for joint dislocation, 0.945 for osteoarthritis, and 0.800 for periarticular calcifications. It also detected osteosynthesis and endoprosthesis with near perfect accuracy (AUC 0.998 and 1.0, respectively). Sensitivity and specificity were 0.75 and 0.86 for fractures, 0.95 and 0.65 for joint dislocation, 0.90 and 0.86 for osteoarthrosis, and 0.60 and 0.89 for calcification. CONCLUSION: CNNs have the potential to serve as an assistive device by providing clinicians a means to prioritize worklists or providing additional safety in situations of increased workload.

Cooling Effects Occur in Hepatic Microwave Ablation At Low Vascular Flow Rates and in Close Proximity to Liver Vessels - Ex Vivo.

Background. The impact of vascular cooling effects in hepatic microwave ablation (MWA) is controversially discussed. The objective of this study was a systematic assessment of vascular cooling effects in hepatic MWA ex vivo. Methods. Microwave ablations were performed in fresh porcine liver ex vivo with a temperature-controlled MWA generator (902-928 MHz) and a non-cooled 14-G-antenna. Energy input was set to 9.0 kJ. Hepatic vessels were simulated by glass tubes. Three different vessel diameters (3.0, 5.0, 8.0 mm) and vessel to antenna distances (5, 10, 20 mm) were examined. Vessels were perfused with saline solution at nine different flow rates (0-500 mL/min). Vascular cooling effects were assessed at the largest cross-sectional ablation area. A quantitative and semi-quantitative/morphologic analysis was carried out. Results. 228 ablations were performed. Vascular cooling effects were observed at close (5 mm) and medium (10 mm) antenna to vessel distances (P < .05). Vascular cooling effects occurred around vessels with flow rates ≥1.0 mL/min (P < .05) and a vessel diameter ≥3 mm (P < .05). Higher flow rates did not result in more distinct cooling effects (P > .05). No cooling effects were measured at large (20 mm) antenna to vessel distances (P > .05). Conclusion. Vascular cooling effects occur in hepatic MWA and should be considered in treatment planning. The vascular cooling effect was mainly affected by antenna to vessel distance. Vessel diameter and vascular flow rate played a minor role in vascular cooling effects.

Periportal fields cause stronger cooling effects than veins in hepatic microwave ablation: an in vivo porcine study.

BACKGROUND: Vascular cooling effects are a well-known source for tumor recurrence in thermal in situ ablation techniques for hepatic malignancies. Microwave ablation (MWA) is an ablation technique to be considered in the treatment of malignant liver tumors. The impact of vascular cooling in MWA is still controversial. PURPOSE: To evaluate the influence of different intrahepatic vessel types, vessel sizes, and vessel-to-antenna-distances on MWA geometry in vivo. MATERIAL AND METHODS: Five MWAs (902-928 MHz) were performed with an energy input of 24.0 kJ in three porcine livers in vivo. MWA lesions were cut into 2-mm slices. The minimum and maximum radius of the ablation area was measured for each slice. Distances were measured from ablation center toward all adjacent hepatic vessels with a diameter of ≥1 mm and within a perimeter of 20 mm around the antenna. The respective vascular cooling effect relative to the maximum ablation radius was calculated. RESULTS: In total, 707 vessels (489 veins, 218 portal fields) were detected; 370 (76%) hepatic veins and 185 (85%) portal fields caused a cooling effect. Portal fields resulted in higher cooling effects (37%) than hepatic veins (26%, P < 0.01). No cooling effect could be observed in close proximity of vessels within the central ablation zone. CONCLUSION: Hepatic vessels influenced MWA zones and caused a distinct cooling effect. Portal fields resulted in more pronounced cooling effect than hepatic veins. No cooling effect was observed around vessels situated within the central white zone.

CT-based quantification of short-term tissue shrinkage following hepatic microwave ablation in an in vivo porcine liver model.

BACKGROUND: Microwave ablation (MWA) is a minimally invasive treatment option for solid tumors and belongs to the local ablative therapeutic techniques, based on thermal tissue coagulation. So far there are mainly ex vivo studies that describe tissue shrinkage during MWA. PURPOSE: To characterize short-term volume changes of the ablated zone following hepatic MWA in an in vivo porcine liver model using contrast-enhanced computer tomography (CECT). MATERIAL AND METHODS: We performed multiple hepatic MWA with constant energy parameters in healthy, narcotized and laparotomized domestic pigs. The volumes of the ablated areas were calculated from venous phase CT scans, immediately after the ablation and in short-term courses of up to 2 h after MWA. RESULTS: In total, 19 thermally ablated areas in 10 porcine livers could be analyzed (n = 6 with two volume measurements during the measurement period and n = 13 with three measurements). Both groups showed a statistically significant but heterogeneous volume reduction of up to 12% (median 6%) of the ablated zones in CECT scans during the measurement period (P < 0.001 [n = 13] and P = 0.042 [n = 6]). However, the dimension and dynamics of volume changes were heterogenous both absolutely and relatively. CONCLUSION: We observed a significant short-term volume reduction of ablated liver tissue in vivo. This volume shrinkage must be considered in clinical practice for technically successful tumor treatment by MWA and therefore it should be further investigated in in vivo studies.

Immediate post-interventional contrast-enhanced computed tomography overestimates hepatic microwave ablation - an in vivo animal study.

Objectives: Contrast-enhanced computed tomography (CECT) is used to monitor technical success immediately after hepatic microwave ablation (MWA). However, it remains unclear, if CECT shows the exact extend of the thermal destruction zone, or if tissue changes such as peri-lesionary edema are depicted as well. The objective of this study was to correlate immediate post-interventional CECT with histological and macroscopic findings in hepatic MWA in porcine liver in vivo.Methods: Eleven MWA were performed in porcine liver in vivo with a microwave generator (928 MHz; energy input 24 kJ). CECT was performed post-interventionally. Livers were explanted and ablations were bisected immediately after ablation. Samples were histologically analyzed after vital staining (NADH-diaphorase). Ablation zones were histologically and macroscopically outlined. We correlated histologic findings, macroscopic images and CECT.Results: Three ablation zones were identified in histological and macroscopic findings. Only one ablation zone could be depicted in CECT. Close conformity was observed between histological and macroscopic findings. The ablation zone depicted in CECT overestimated the histological avital central zone and inner red zone (p < = .01). No differences were found between CECT and the histological outer red zone (p > .05).Conclusions: Immediate post-interventional CECT overestimated the clinically relevant zone of complete cell ablation after MWA in porcine liver in vivo. This entails the risk of incomplete tumor ablation and could lead to tumor recurrence.

Is lung density associated with severity of COVID-19?

PURPOSE: Emphysema and chronic obstructive lung disease were previously identified as major risk factors for severe disease progression in COVID-19. Computed tomography (CT)-based lung-density analysis offers a fast, reliable, and quantitative assessment of lung density. Therefore, we aimed to assess the benefit of CT-based lung density measurements to predict possible severe disease progression in COVID-19. MATERIAL AND METHODS: Thirty COVID-19-positive patients were included in this retrospective study. Lung density was quantified based on routinely acquired chest CTs. Presence of COVID-19 was confirmed by reverse transcription polymerase chain reaction (RT-PCR). Wilcoxon test was used to compare two groups of patients. A multivariate regression analysis, adjusted for age and sex, was employed to model the relative increase of risk for severe disease, depending on the measured densities. RESULTS: Intensive care unit (ICU) patients or patients requiring mechanical ventilation showed a lower proportion of medium- and low-density lung volume compared to patients on the normal ward, but a significantly larger volume of high-density lung volume (12.26 dl IQR 4.65 dl vs. 7.51 dl vs. IQR 5.39 dl, p = 0.039). In multivariate regression analysis, high-density lung volume was identified as a significant predictor of severe disease. CONCLUSIONS: The amount of high-density lung tissue showed a significant association with severe COVID-19, with odds ratios of 1.42 (95% CI: 1.09-2.00) and 1.37 (95% CI: 1.03-2.11) for requiring intensive care and mechanical ventilation, respectively. Acknowledging our small sample size as an important limitation; our study might thus suggest that high-density lung tissue could serve as a possible predictor of severe COVID-19.

Comparison of different 4D CT-Perfusion algorithms to visualize lesions after microwave ablation in an in vivo porcine model.

Background: Accurate lesion visualization after microwave ablation (MWA) remains a challenge. Computed tomography perfusion (CTP) has been proposed to improve visualization, but it was shown that different perfusion-models delivered different results on the same data set.Purpose: Comparison of different perfusion algorithms and identification of the algorithm enables for the best imaging of lesion after hepatic MWA.Materials and methods: 10 MWA with consecutive CTP were performed in healthy pigs. Parameter-maps were generated using a single-input-dual-compartment-model with Patlak's algorithm (PM), a dual-input-maximum-slope-model (DIMS), a dual-input-one-compartment-model (DIOC), a single-(SIDC) and dual-input-deconvolution-model (DIDC). Parameter-maps for hepatic arterial (AF) and portal venous blood flow (PF), mean transit time, hepatic blood volume (HBV) and capillary permeability were compared regarding the values of the normal liver tissue (NLT), lesion, contrast- and signal-to-noise ratios (SNR, CNR) and inter- and intrarater-reliability using the intraclass correlation coefficient, Bland-Altman plots and linear regression.Results: Perfusion values differed between algorithms with especially large fluctuations for the DIOC. A reliable differentiation of lesion margin appears feasible with parameter-maps of PF and HBV for most algorithms, except for the DIOC due to large fluctuations in PF. All algorithms allowed for a demarcation of the central necrotic zone based on hepatic AF and HBV. The DIDC showed the highest CNR and the best inter- and intrarater reliability.Conclusion: The DIDC appears to be the most feasible model to visualize margins and necrosis zones after microwave ablation, but due to high computational demand, a single input deconvolution algorithm might be preferable in clinical practice.

Incidence of combined cranial and cervical spine injuries in patients with blunt minor trauma: are combined CT examinations of the head and cervical spine justified?

Background The use of computed tomography (CT) scans of the head and cervical spine has markedly increased in patients with blunt minor trauma. The actual likelihood of a combined injury of head and cervical spine following a minor trauma is estimated to be low. Purpose To determine the incidence of such combined injuries in patients with a blunt minor trauma in order to estimate the need to derive improved diagnostic guidelines. Material and Methods A total of 1854 patients were retrospectively analyzed. All cases presented to the emergency department and in all patients combined CT scans of head and cervical spine were conducted. For the following analysis, only 1342 cases with assured blunt minor trauma were included. Data acquisition covered age, sex, and presence of a head injury as well as presence of a cervical spine injury or both. Results Of the 1342 cases, 46.9% were men. The mean age was 65.6 years. CT scans detected a head injury in 116 patients; of these, 70 cases showed an intracranial hemorrhage, 11 cases a skull fracture, and 35 cases an intracranial hemorrhage as well as a skull fracture. An injury of the cervical spine could be detected in 40 patients. A combined injury of the head and cervical spine could be found in one patient. Conclusion The paradigm of the coincidence of cranial and cervical spine injuries should be revised in patients with blunt minor trauma. Valid imaging decision algorithms are strongly needed to clinically detect high-risk patients in order to save limited resources.

CT-based measurement of the inner pelvic volume.

Background Surgery in the lesser pelvis is associated with a high complication rate as surgeons are spatially limited by solid anatomic structures and soft tissue borders. So far, only two-dimensional (2D) parameters have been used for risk stratification. Purpose To precisely measure the inner pelvic volume a computed tomography (CT)-based three-dimensional (3D) approach was established and compared to approximations by 2D parameter combinations. Material and Methods Thin-layered multi-slice CT datasets were used retrospectively for slice by slice depiction of the inner pelvic surface. The inner pelvic volume was then automatically compounded. Combinations of two to four 2D dimensions determined in 3D volume rendered reconstructions were correlated with the inner pelvic volume. Pearson's correlation coefficient and Chi square test were used for statistical calculations. Significance level was set at P < 0.05. Results In total 142 patients (91 men, 51 women) aged 64.8 ± 10.6 years at surgery were included in the study. Mean calculated pelvic volume was 1031.13 ± 180.06 cm3 (men, 996.57 ± 172.43 cm3; women, 1093.34 ± 178.39 cm3). Best approximations were obtained by combination of the 2D measurements transverse inlet and pelvic height for men (r = 0.799, P < 0.05) as well as transverse inlet, obstetric conjugate, interspinous distance and pelvic depth for women (r = 0.855, P < 0.05). Conclusion We describe a precise and reproducible CT-based method for pelvic volumetry. A less time consuming but still reliable approximation can be achieved by combination of two to four 2D dimensions.

Determination of the electrical conductivity of human liver metastases: impact on therapy planning in the radiofrequency ablation of liver tumors.

Background Radiofrequency ablation is used to induce thermal necrosis in the treatment of liver metastases. The specific electrical conductivity of a liver metastasis has a distinct influence on the heat formation and resulting tumor ablation within the tissue. Purpose To examine the electrical conductivity σ of human colorectal liver metastases and of tumor-free liver tissue in surgical specimens. Material and Methods Surgical specimens from patients with resectable colorectal liver metastases were used for measurements (size of metastases <30 mm). A four-needle measuring probe was used to determine the electrical conductivity σ of human colorectal liver metastasis (n = 8) and tumor-free liver tissue (n = 5) in a total of five patients. All measurements were performed at 470 kHz, which is the relevant frequency for radiofrequency ablation. The tissue temperature was also measured. Hepatic resections were performed in accordance with common surgical standards. Measurements were performed in the operating theater immediately after resection. Results The median electrical conductivity σ was 0.57 S/m in human colorectal liver metastases at a median temperature of 35.1℃ and 0.35 S/m in tumor-free liver tissue at a median temperature of 34.9℃. The electrical conductivity was significantly higher in tumor tissue than in tumor-free liver tissue ( P = 0.005). There were no differences in tissue temperature between the two groups ( P = 0.883). Conclusion The electrical conductivity is significantly higher in human colorectal liver metastases than in tumor-free liver tissue at a frequency of 470 kHz.

[Severe Bleeding from the Middle Ear Cavity after Myringotomy: Review Based on a Case Report]. / Massive Blutung bei einer Parazentese: Übersicht anhand eines Fallberichts.

Report of a rare case of severe bleeding from the middle ear cavity after myringotomy. On the basis of the case report, the procedure for such bleeding is discussed in the context of the literature. A 6-year-old boy received a revision myringotomy in an ambulant setting. During the procedure a severe bleeding occurred. The external auditory canal was adequately packed. The patient was extubated and transferred to the clinic as an emergency. Computer tomography of the temporal bone showed the anatomical variant of a dehiscent high jugular bulb, which had been injured. Because no rebleeding occurred, the packing of the ear canal was removed and an explorative tympanoscopy was performed on the third postoperative day. When the tympanomeatal flap was lifted, the defect in the jugular bulb was found. The lesion was covered with Tutopatch® pads and fibrin glue and the auditory canal was packed again. After removal of the packing three weeks postoperatively a properly healed situs was found. No further measures were taken. The injury of a dehiscent jugular bulb in the course of ear surgeries leads to a massive hemorrhage. The case describes the diagnostic and therapeutic procedure for this relatively rare but severe complication.

Open Access Data and Deep Learning for Cardiac Device Identification on Standard DICOM and Smartphone-based Chest Radiographs.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop and evaluate a publicly available deep learning model for segmenting and classifying cardiac implantable electronic devices (CIEDs) on Digital Imaging and Communications in Medicine (DICOM) and smartphone-based chest radiograph (CXR) images. Materials and Methods This institutional review board-approved retrospective study included patients with implantable pacemakers, cardioverter defibrillators, cardiac resynchronization therapy devices, and cardiac monitors who underwent chest radiography between January 2012 and January 2022. A U-Net model with a ResNet-50 backbone was created to classify CIEDs on DICOM and smartphone images. Using 2,321 CXRs from 897 patients (median age, 76 years (range 18-96 years); 625 male, 272 female), CIEDs were categorized into four manufacturers, 27 models, and one 'other' category. Five smartphones were used to acquire 11,072 images. Performance was reported using the Dice coefficient on the validation set for segmentation or balanced accuracy on the test set for manufacturer and model classification, respectively. Results The segmentation tool achieved a mean Dice coefficient of 0.936 (IQR: 0.890-0.958). The model had an accuracy of 94.36% (95% CI: 90.93%-96.84%; n = 251/266) for CIED manufacturer classification and 84.21% (95% CI: 79.31%-88.30%; n = 224/266) for CIED model classification. Conclusion The proposed deep learning model, trained on both traditional DICOM and smartphone images, showed high accuracy for segmentation and classification of CIEDs on CXRs. ©RSNA, 2024.

Dual center validation of deep learning for automated multi-label segmentation of thoracic anatomy in bedside chest radiographs.

BACKGROUND AND OBJECTIVES: Bedside chest radiographs (CXRs) are challenging to interpret but important for monitoring cardiothoracic disease and invasive therapy devices in critical care and emergency medicine. Taking surrounding anatomy into account is likely to improve the diagnostic accuracy of artificial intelligence and bring its performance closer to that of a radiologist. Therefore, we aimed to develop a deep convolutional neural network for efficient automatic anatomy segmentation of bedside CXRs. METHODS: To improve the efficiency of the segmentation process, we introduced a "human-in-the-loop" segmentation workflow with an active learning approach, looking at five major anatomical structures in the chest (heart, lungs, mediastinum, trachea, and clavicles). This allowed us to decrease the time needed for segmentation by 32% and select the most complex cases to utilize human expert annotators efficiently. After annotation of 2,000 CXRs from different Level 1 medical centers at Charité - University Hospital Berlin, there was no relevant improvement in model performance, and the annotation process was stopped. A 5-layer U-ResNet was trained for 150 epochs using a combined soft Dice similarity coefficient (DSC) and cross-entropy as a loss function. DSC, Jaccard index (JI), Hausdorff distance (HD) in mm, and average symmetric surface distance (ASSD) in mm were used to assess model performance. External validation was performed using an independent external test dataset from Aachen University Hospital (n = 20). RESULTS: The final training, validation, and testing dataset consisted of 1900/50/50 segmentation masks for each anatomical structure. Our model achieved a mean DSC/JI/HD/ASSD of 0.93/0.88/32.1/5.8 for the lung, 0.92/0.86/21.65/4.85 for the mediastinum, 0.91/0.84/11.83/1.35 for the clavicles, 0.9/0.85/9.6/2.19 for the trachea, and 0.88/0.8/31.74/8.73 for the heart. Validation using the external dataset showed an overall robust performance of our algorithm. CONCLUSIONS: Using an efficient computer-aided segmentation method with active learning, our anatomy-based model achieves comparable performance to state-of-the-art approaches. Instead of only segmenting the non-overlapping portions of the organs, as previous studies did, a closer approximation to actual anatomy is achieved by segmenting along the natural anatomical borders. This novel anatomy approach could be useful for developing pathology models for accurate and quantifiable diagnosis.