Experienced Pathologist Research Articles

A Deep Learning–Based System Trained for Gastrointestinal Stromal Tumor Screening Can Identify Multiple Types of Soft Tissue Tumors

The accuracy and timeliness of the pathologic diagnosis of soft tissue tumors (STTs) critically affect treatment decision and patient prognosis. Thus, it is crucial to make a preliminary judgement on whether the tumor is benign or malignant with hematoxylin-and-eosin-stained images. Here, we present a deep learning-based system, STT-BOX, with only hematoxylin-and-eosin images for malignant STT identification from benign STTs with histopathologic similarity. STT-BOX assumed gastrointestinal stromal tumor as a baseline for malignant STT evaluation, and distinguished gastrointestinal stromal tumor from leiomyoma and schwannoma with 100% area under the curve in patients from three hospitals, which achieved higher accuracy than the interpretation of experienced pathologists. Particularly, this system performed well on six common types of malignant STTs from The Cancer Genome Atlas data set, accurately highlighting the malignant mass lesion. Moreover, without any fine-tuning, STT-BOX was capable of distinguishing ovarian malignant sex-cord stromal tumors. Our study includes mesenchymal tumors that originated from the digestive system, bone and soft tissues, and reproductive system, where the high accuracy of migration verification may reveal the morphologic similarity of the nine types of malignant tumors. Further evaluation in a pan-STT setting would be potential and prospective, obviating the overuse of immunohistochemistry and molecular tests, and providing a practical basis for clinical treatment selection in a timely manner.

Extreme learning machine algorithm‐based model for lung cancer classification from histopathological real‐time images

AbstractIn the present scenario, developing an automatic and credible diagnostic system to analyze lung cancer type, stage, and level from computed tomography (C.T.) images is a very challenging task, even for experienced pathologists, due to the nonuniform illumination and artifacts. The nonuniform illumination and artifacts are the low‐frequency changes in image intensity that arise from the sensor and the person's movement while recording the C.T. scanned images. Although numerous machine learning techniques are used to improve the effectiveness of automatic lung cancer diagnostic systems, the classification accuracy of these systems still needs significant improvement to satisfy the real‐time requirement of the diagnostic situations. A new extreme learning machine (ELM) algorithm‐based model (hereafter called XlmNet) is proposed to classify the histopathology scans effectively. XlmNet utilizes The Cancer Imaging Archive (TCIA) dataset. After data collection, the initial stage in XlmNet is preprocessing, including noise removal, histogram equalization, and quality‐improved image. The enhanced Profuse Clustering (EPC) method is implemented for segmenting the affected regions from C.T. scans by image segment using superpixel clustering. The statistical attributes are extracted by using Principal Component Analysis (PCA). ELM classifier helps in classifying the lung nodules. The empirical results of the XlmNet model are related to some advanced classifiers concerning performance metrics. The evaluations of XlmNet on the TCIA dataset reveal that XlmNet outperforms other classification networks with the Accuracy of 0.965, a sensitivity of 0.964, a specificity of 0.865, a precision of 0.962, a Jaccard similarity score (JSS) of 0.95.

Abstract 5993: Dissecting the role of CDK4 in liposarcoma

Abstract Background: Liposarcoma (LPS) encompass one of most common soft tissue sarcoma (STS) histological subtypes accounting for 15% of all cases. They can be divided into four entities including atypical lipomatous tumor or well-differentiated LPS (ALT/WDLPS), dedifferentiated LPS (DDLPS), myxoid LPS (MLPS) and pleomorphic LPS (PLS). Their heterogeneity is reflected in their morphology, molecular landscape, prognosis and clinical behavior. Current treatment options for localized disease include surgery, (neo)adjuvant radiotherapy and chemotherapy; for the metastatic setting chemotherapy represents the cornerstone but its role needs to be elucidated. The unavailability of predictive biomarkers makes the management of these mesenchymal diseases even more challenging. Recent evidences have shed light on the key role of cyclin-dependent kinase 4 (CDK4) in cancer cells proliferation. Methods: This study involved 21 adult patients affected by liposarcoma (n= 5 ALT/WDLPS and n= 16 DDLPS). IHC analyses of CDK4 and MDM2 were performed on FFPE surgically resected specimens by experienced sarcoma pathologists. Moreover, a patient-derived DDLPS cell line was established and pharmacological profiling was performed. Next, standard and innovative drugs currently used for LPS management were assessed in both 2D and 3D culture systems. Finally, in silico analyses based on public repositories on STS were carried out including 260 sarcoma patients. Results: IHC results highlighted an higher expression of CDK4 and MDM2 biomarkers in DDLPS compared to ALT/WDLSP. Moreover, although CDK4 and MDM2 are codified by the same genomic region, their expression did not correlate (e.g. 100% of CDK4 and 2% of MDM2 in the same patient). Therefore we hypothesized that even exhibiting a low MDM2 expression, a patient could benefit from CDK4 inhibition. Furthermore, our data showed that in DDLPS cases CDK4 expression ranged from 90% to 100% in spermatic cord, skin and abdomen while it ranged from 5% to 50% in the retroperitoneum. The above observations prompted us to hypothesize a correlation of CDK4 expression with specific anatomical sites. Moreover, pharmacological profiling showed a synergistic effect exerted by sequential treatment with palbociclib and some chemotherapeutics including trabectedin, dacarbazine, eribulin and lenvatinib. Indeed we observed a 10% increase in cell proliferation inhibition compared to standard treatment. In addition, in silico analyses revealed the role of CDK4 as negative prognostic biomarker for PFS and OS in STS. Conclusions: Our study highlighted the promising role of CDK4 in the management of LPS patients. In particular, we pointed out encouraging results of sequential treatment combining CDK4 inhibitor palbociclib and chemotherapy. Furthermore, preliminary analyses highlighted the involvement of this biomarker as a potential prognostic tool for STS. Citation Format: Silvia Vanni, Graziana Gallo, Valentina Fausti, Giacomo Miserocchi, Chiara Liverani, Chiara Spadazzi, Claudia Cocchi, Chiara Calabrese, Giovanni De Luca, Massimo Bassi, Manlio Gessaroli, Angelo Campobassi, Federica Pieri, Giorgio Ercolani, Davide Cavaliere, Lorena Gurrieri, Nada Riva, Giovanni Martinelli, Laura Mercatali, Alessandro De Vita. Dissecting the role of CDK4 in liposarcoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5993.

MP78-07 THE PREVALENCE OF CONCOMITANT SQUAMOUS METAPLASIA IN BULBAR URETHRAL STRICTURES AND ITS ASSOCIATION WITH RECONSTRUCTIVE DELAY AND URETHRAL REST BY SUPRAPUBIC URINARY DIVERSION

You have accessJournal of UrologyCME1 Apr 2023MP78-07 THE PREVALENCE OF CONCOMITANT SQUAMOUS METAPLASIA IN BULBAR URETHRAL STRICTURES AND ITS ASSOCIATION WITH RECONSTRUCTIVE DELAY AND URETHRAL REST BY SUPRAPUBIC URINARY DIVERSION Yusuke Hirano, Akio Horiguchi, Masayuki Shinchi, Kenichiro Ojima, Keiichi Ito, Ryuichi Azuma, and Kosuke Miyai Yusuke HiranoYusuke Hirano More articles by this author , Akio HoriguchiAkio Horiguchi More articles by this author , Masayuki ShinchiMasayuki Shinchi More articles by this author , Kenichiro OjimaKenichiro Ojima More articles by this author , Keiichi ItoKeiichi Ito More articles by this author , Ryuichi AzumaRyuichi Azuma More articles by this author , and Kosuke MiyaiKosuke Miyai More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003355.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Squamous metaplasia (SM) of a normal urethral epithelium is considered to be the initial pathological change associated with a urethral stricture. SM proximal to a stricture is a severe problem that may affect the choice of urethroplasty technique and surgical outcome. A histological analysis was conducted to evaluate the prevalence of SM in bulbar urethral strictures and the association with delayed reconstruction and urethral rest. METHODS: We reviewed the records of 169 male patients with bulbar urethral strictures who had undergone excision and primary anastomosis (EPA) between 2010 and 2020 by a single surgeon (AH). Those with prior urethroplasty and incomplete data were excluded from analysis. The proximal edges of formalin-fixed, paraffin-embedded urethral sections were sliced axially, and ImageJ measured the ratio of the urethral lumen in which SM was present on HE-stained slides by an experienced pathologist (MK). Reconstructive delay was calculated as the time between the initial stricture diagnosis and EPA. Urethral rest was accomplished by suprapubic tube placement and cessation of voiding at least three months before EPA. The success of EPA was defined as the absence of need for additional treatment. RESULTS: SM was present in 88 patients (52.1%), and the median ratio of SM in the urethral lumen was 13.5%. The median reconstructive delay in patients with SM (40 months) was significantly longer than that in patients without SM (9 months, p<0.0001). Patients with SM were significantly more likely to have had no urethral rest (p<0.0001) and undergo repeat transurethral treatments such as urethrotomy and dilation before EPA (p=0.0002). Multivariate logistic regression analysis showed that a longer reconstructive delay (for every month: odds ratio (OR) 1.01, 95% confidence interval (CI) 1.00–1.02, p=0.004) and absence of urethral rest (OR 3.82, 95% CI 1.59–9.20, p=0.002) were significant predictors of concomitant SM. There was no significant difference in the success rates between patients with SM (82 of 88, 93.1%) and those without SM (79 of 81, 97.5%, p=0.18). CONCLUSIONS: Persistent chronic high-pressure voiding due to reconstructive delay and absence of urethral rest are responsible for concomitant SM. Source of Funding: Nothing © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e1134 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Yusuke Hirano More articles by this author Akio Horiguchi More articles by this author Masayuki Shinchi More articles by this author Kenichiro Ojima More articles by this author Keiichi Ito More articles by this author Ryuichi Azuma More articles by this author Kosuke Miyai More articles by this author Expand All Advertisement PDF downloadLoading ...

Is decreased tissue elasticity more important than histopathological changes in ruptures of ascending aortic aneurysms?

In this study, we aimed to investigate the natural properties of ascending aortic aneurysms and to compare dilated aortic tissues of patients with ascending aortic aneurysms and the non-pathological aortic tissues of cadavers. Between January 2017 and January 2020, a total of 14 patients (12 males, 2 females; mean age: 66.6±8.4 years; range, 54 to 77 years) who underwent elective ascending aortic surgery without genetic disease or family history in the etiology were included in the study. Aortic tissues taken from the patients with ascending aortic aneurysms and cadavers without any aortic pathology were compared histopathologically and biomechanically. An experienced pathologist performed a histological evaluation with appropriate staining and scoring. In the biomechanical examination, stress-strain curves were created with the uniaxial tensile test. The instantaneous elastic modulus was calculated based on the first regions of the curves (Ei) and the slopes of the linear region close to rupturing (Es). In the pathological examination, there was no statistically significant difference in the parameters of both the patient and control groups (p>0.05). In the biomechanical examination, Ei values were significantly higher in the patients with ascending aortic aneurysms, while Es values were comparable between the groups (p=0.028 and p=0.609, respectively). Our study results showed that the tissues of the ascending aortic aneurysm were much more rigid, although no significant histopathological changes were detected. These findings are meaningful in understanding the structure of normal and pathological aortic tissue.

MSHT: Multi-stage Hybrid Transformer for the ROSE Image Analysis of Pancreatic Cancer.

Pancreatic cancer is one of the most malignant cancers with high mortality. The rapid on-site evaluation (ROSE) technique can significantly accelerate the diagnostic workflow of pancreatic cancer by immediately analyzing the fast-stained cytopathological images with on-site pathologists. However, the broader expansion of ROSE diagnosis has been hindered by the shortage of experienced pathologists. Deep learning has great potential for the automatic classification of ROSE images in diagnosis. But it is challenging to model the complicated local and global image features. The traditional convolutional neural network (CNN) structure can effectively extract spatial features, while it tends to ignore global features when the prominent local features are misleading. In contrast, the Transformer structure has excellent advantages in capturing global features and long-range relations, while it has limited ability in utilizing local features. We propose a multi-stage hybrid Transformer (MSHT) to combine the strengths of both, where a CNN backbone robustly extracts multi-stage local features at different scales as the attention guidance, and a Transformer encodes them for sophisticated global modeling. Going beyond the strength of each single method, the MSHT can simultaneously enhance the Transformer global modeling ability with the local guidance from CNN features. To evaluate the method in this unexplored field, a dataset of 4240 ROSE images is collected where MSHT achieves 95.68% in classification accuracy with more accurate attention regions. The distinctively superior results compared to the state-of-the-art models make MSHT extremely promising for cytopathological image analysis. The codes and records are available at: https://github.com/sagizty/ Multi-Stage-Hybrid-Transformer.

Undetectable anti-Mullerian hormone and inhibin B do not preclude the presence of germ cell tumours in 45,X/46,XY or 46,XY gonadal dysgenesis.

Individuals with 45,X/46,XYor 46,XYgonadal dysgenesis are at increased risk of germ cell malignancies. Therefore, prophylactic bilateral gonadectomy is advised in girls and considered in boys with atypical genitalia for undescended, macroscopically abnormal gonads. However, severely dysgenetic gonads may not contain germ cells rendering gonadectomy unnecessary. Therefore, we investigate if undetectable preoperative serum anti-Müllerian hormone(AMH) and inhibin B can predict the absence of germ cells, (pre)malignant or otherwise. Individuals who had undergone bilateral gonadal biopsy and/or gonadectomy because of suspected gonadal dysgenesis in 1999-2019 were included in this retrospective study if preoperative AMH and/or inhibin B were available. Histological material was reviewed by an experienced pathologist. Haematoxylin and eosin and immunohistochemical stainings for SOX9, OCT4, TSPY and SCF (KITL) were used. Thirteen males and 16 females were included, 20 with 46,XYand 9 with 45,X/46,XY DSD. Threefemales had dysgerminoma alongside gonadoblastoma; twogonadoblastoma, one germ cell neoplasia in situ (GCNIS) and three males had pre-GCNIS and/or pre-gonadoblastoma. Gonadoblastoma and/or dysgerminoma were present in 3/11 individuals with undetectable AMH and inhibin B, one of whom also had non-(pre)malignant germ cells. Of the other 18, in whom AMH and/or inhibin B were detectable, only one had no germ cells. Undetectable serum AMH and inhibin B cannot reliably predict the absence of germ cells and germ cell tumours in individuals with 45,X/46,XYor 46,XYgonadal dysgenesis. This information should help in counselling about prophylactic gonadectomy, taking into account both the germ cell cancer risk and potential for gonadal function.

Deep Learning Approach to Classify Cutaneous Melanoma in a Whole Slide Image.

Although the histopathological diagnosis of cutaneous melanocytic lesions is fairly accurate and reliable among experienced surgical pathologists, it is not perfect in every case (especially melanoma). Microscopic examination-clinicopathological correlation is the gold standard for the definitive diagnosis of melanoma. Pathologists may encounter diagnostic controversies when melanoma closely mimics Spitz's nevus or blue nevus, exhibits amelanotic histopathology, or is in situ. It would be beneficial if diagnosing cutaneous melanocytic lesions can be automated by using deep learning, particularly when assisting surgical pathologists with their workloads. In this preliminary study, we investigated the application of deep learning for classifying cutaneous melanoma in whole-slide images (WSIs). We trained models via weakly supervised learning using a dataset of 66 WSIs (33 melanomas and 33 non-melanomas). We evaluated the models on a test set of 90 WSIs (40 melanomas and 50 non-melanomas), achieving ROC-AUC at 0.821 for the WSI level and 0.936 for the tile level by the best model.

Classification of Chondrosarcoma: From Characteristic to Challenging Imaging Findings.

Chondrosarcomas can be classified into various forms according to the presence or absence of a precursor lesion, location, and histological subtype. The new 2020 World Health Organization (WHO) Classification of Tumors of Soft Tissue and Bone classifies chondrogenic bone tumors as benign, intermediate (locally aggressive), or malignant, and separates atypical cartilaginous tumors (ACTs) and chondrosarcoma grade 1 (CS1) as intermediate and malignant tumors. respectively. Furthermore, the classification categorizes chondrosarcomas (including ACT) into eight subtypes: central conventional (grade 1 vs. 2-3), secondary peripheral (grade 1 vs. 2-3), periosteal, dedifferentiated, mesenchymal, and clear cell chondrosarcoma. Most chondrosarcomas are the low-grade, primary central conventional type. The rarer subtypes include clear cell, mesenchymal, and dedifferentiated chondrosarcomas. Comprehensive analysis of the characteristic imaging findings can help differentiate various forms of chondrosarcomas. However, distinguishing low-grade chondrosarcomas from enchondromas or high-grade chondrosarcomas is radiologically and histopathologically challenging, even for experienced radiologists and pathologists.

Teledermatopathology on the ADOI platform and beyond.

In September 2021 on the platform of the Italian Association of Hospital Dermatologists (ADOI; https://www.adoi.it/) a monthly on-line live session of teledermatopathology was started involving collegial discussion among experienced Italian pathologists about challenging melanocytic tumors using the virtual microscope [...].

Recognizing Minor Leukemic Populations with Monocytic Features in Mixed-Phenotype Acute Leukemia by Flow Cell Sorting Followed by Cytogenetic and Molecular Studies: Report of Five Exemplary Cases

Mixed-phenotype acute leukemia (MPAL), a rare and heterogeneous category of acute leukemia, is characterized by cross-lineage antigen expression. Leukemic blasts in MPAL can be represented either by one population with multiple markers of different lineages or by several single-lineage populations. In some cases, a major blast population may coexist with a smaller population that has minor immunophenotypic abnormalities and may be missed even by an experienced pathologist. To avoid misdiagnosis, we suggest sorting doubtful populations and leukemic blasts and searching for similar genetic aberrations. Using this approach, we examined questionable monocytic populations in five patients with dominant leukemic populations of B-lymphoblastic origin. Cell populations were isolated either for fluorescence in situ hybridization or for clonality assessment by multiplex PCR or next-generation sequencing. In all cases, monocytic cells shared the same gene rearrangements with dominant leukemic populations, unequivocally confirming the same leukemic origin. This approach is able to identify implicit cases of MPAL and therefore leads to the necessary clinical management for patients.

Comparative study of deep learning models on the images of biopsy specimens for diagnosis of lung cancer treatment

ObjectiveTo compare the application value of different traditional deep learning models in diagnosing and classifying lung cancer. MethodsAccording to the biopsy samples of our hospital from January 2018 to November 2022, 37 patients treated in this department were selected as the study subjects. Nonsmall and small cell lung cancer biopsy specimens were obtained and stained. Two experienced pathologists diagnosed the biopsy specimens. Multiple in-depth learning methods were used to distinguish between cancer and noncancer biopsies. In this study, we compared the application value of traditional deep learning models in lung cancer diagnosis and Classification. ResultsThe study tested several popular CNN architectures based on image block classification: AlexNet, VGG, ResNet, and SqueezeNet, comparing two types of training schemes: training from scratch and fine-tuning the entire pretrained network. The AUC of the deep learning model is more reasonable (0. 8808–0. 9121). Except for Resnet-50, the AUC of the training is higher than that of the fine-tuning of the whole network. ConclusionDeep learning analysis can accelerate the detection speed of whole section images (WSI) and maintain a similar detection rate with pathologists.

Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features.

Acute lymphoblastic leukemia (ALL) is one of the deadliest forms of leukemia due to the bone marrow producing many white blood cells (WBC). ALL is one of the most common types of cancer in children and adults. Doctors determine the treatment of leukemia according to its stages and its spread in the body. Doctors rely on analyzing blood samples under a microscope. Pathologists face challenges, such as the similarity between infected and normal WBC in the early stages. Manual diagnosis is prone to errors, differences of opinion, and the lack of experienced pathologists compared to the number of patients. Thus, computer-assisted systems play an essential role in assisting pathologists in the early detection of ALL. In this study, systems with high efficiency and high accuracy were developed to analyze the images of C-NMC 2019 and ALL-IDB2 datasets. In all proposed systems, blood micrographs were improved and then fed to the active contour method to extract WBC-only regions for further analysis by three CNN models (DenseNet121, ResNet50, and MobileNet). The first strategy for analyzing ALL images of the two datasets is the hybrid technique of CNN-RF and CNN-XGBoost. DenseNet121, ResNet50, and MobileNet models extract deep feature maps. CNN models produce high features with redundant and non-significant features. So, CNN deep feature maps were fed to the Principal Component Analysis (PCA) method to select highly representative features and sent to RF and XGBoost classifiers for classification due to the high similarity between infected and normal WBC in early stages. Thus, the strategy for analyzing ALL images using serially fused features of CNN models. The deep feature maps of DenseNet121-ResNet50, ResNet50-MobileNet, DenseNet121-MobileNet, and DenseNet121-ResNet50-MobileNet were merged and then classified by RF classifiers and XGBoost. The RF classifier with fused features for DenseNet121-ResNet50-MobileNet reached an AUC of 99.1%, accuracy of 98.8%, sensitivity of 98.45%, precision of 98.7%, and specificity of 98.85% for the C-NMC 2019 dataset. With the ALL-IDB2 dataset, hybrid systems achieved 100% results for AUC, accuracy, sensitivity, precision, and specificity.

A162 RARE PRESENTATION OF EXTRANODAL NK/T-CELL LYMPHOMA INVOLVING STOMACH AND EYE: CASE REPORT AND LITERATURE REVIEW

Abstract Background Extranodal NK/T-cell lymphoma (ENKTL) is a rare and aggressive form of non-Hodgkin lymphoma. ENKTL are predominantly localized to nasal and upper aerodigestive sites, but extranasal involvement including gastrointestinal tract are rarely seen. Small and large intestines are primary sites of gastrointestinal ENKTL. Gastric involvements are exceedingly rare accounting for less than 5% of all gastrointestinal ENKTL. Purpose We present a literature review on gastrointestinal ENKTL and a case report of gastrointestinal bleed secondary to ENKTL involving stomach and left orbit. Method Case report and literature review. Result(s) 33-year-old female was admitted to a tertiary hospital with 3-week history of epigastric pain and left periorbital swelling. Abdominal CT showed edema and thickening of gastric folds. Head CT showed grossly enlarged left lateral rectus muscle and periorbital soft tissue swelling suggestive of left orbital pseudotumor. Esophagogastroduodenoscopy revealed multifocal Forrest classification II-C ulcerations throughout her stomach and duodenum. Biopsies from stomach showed gastric mucosa with extensive infiltration by an atypical lymphoid cell population. Immunohistochemistry demonstrated high grade lymphoid cells with uniform expression of CD2, CD3, CD30, CD56, TIA1, perforin and granzyme B. EBV-encoded small RNA in-situ hybridization (EBER ISH) was strongly positive. Findings were consistent with gastric ENKTL. Left orbital biopsy revealed similar morphology and phenotypic features consistent with concurrent ENKTL involvement of her orbit. Patient was initiated on intravenous corticosteroids, but unfortunately developed hemorrhagic shock secondary to gastrointestinal bleeding from gastric ulcer and passed away. Gastrointestinal ENKTL is a rare presentation of a rare disease. Gastric involvement is especially rare and described only in few case reports. Gastrointestinal ENKTL are often initially asymptomatic but can progress to abdominal pain, bleeding and even bowel perforation. Due its nonspecific clinical features and rarity, diagnosis can be difficult and requires careful examination by an experienced pathologist. This aggressive lymphoma is characterized by positive CD2, CD3, CD30, CD56, TIA, granzyme B, perforin and EBER ISH. Optimal treatment approach remains unclear due to lack of prospective clinical studies. Currently, treatment modalities used for other lymphomas including radiotherapy and non-anthracycline-based chemotherapy are used. Despite treatment, prognosis is grim with median overall survival period of 7-8 months. Image Conclusion(s) We present a case of gastric and orbital ENKTL with gastric ulcer bleeding. Gastric ENKTL disease is a rare presentation of a rare disease. Due to non-specific clinical features, diagnosis is often difficult and relies on careful pathology examination by experienced pathologist. Prognosis is poor without optimal treatment approach due to rarity of disease and lack of validated data. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared INFLAMMATORY BOWEL DISEASES MECHANISMS AND TREATMENTS

Response to Commentary "Are Parenchyma-Sparing Resections Really Appropriate for Small (

Response to Commentary "Are Parenchyma-Sparing Resections Really Appropriate for Small (<3 cm) Non-Functional Pancreatic Neuroendocrine Tumors?"

Imaging of lumpectomy surface with large field-of-view confocal laser scanning microscopy ‘Histolog® scanner’ for breast margin assessment in comparison with conventional specimen radiography

PurposeThe Histolog® Scanner (SamanTree Medical SA, Lausanne, Switzerland) is a large field-of-view confocal laser scanning microscope designed to allow intraoperative margin assessment by the production of histological images ready for assessment in the operating room. We evaluated the feasibility and the performance of the Histolog® Scanner (HS) to correctly identify infiltrated margins in clinical practice of lumpectomy specimens. It was extrapolated if the utilization of the HS has the potential to reduce infiltrated margins and therefore reduce re-operation rates in patients undergoing breast conserving surgery (BCS) due to a primarily diagnosed breast cancer including ductal carcinoma in situ. MethodsThis is a single-center, prospective, non-interventional, diagnostic pilot study including 50 consecutive patients receiving BCS. The complete surface of the specimen was scanned using the HS intraoperatively. The surgery and the intraoperative margin assessment of the specimen was performed according to the clinical routine consisting of conventional specimen radiography as well as the clinical impression of the surgeon. Three surgeons and an experienced pathologist assessed the scans produced by the HS for cancer cells on the surface. The potential of the HS to correctly identify involved margins was compared to the results of the conventional specimen radiography alone as well as the clinical routine. The histopathological report served as the gold standard. Results50 specimens corresponding to 300 surfaces were scanned by the HS. The mean sensitivity of the surgeons to identify involved margins with the HS was 37.5% ± 5.6%, the specificity was 75.2% ± 13.0%. The assessment of resection margins by the pathologist resulted in a sensitivity of 37.5% and a specificity of 81.0%, while the local clinical routine resulted in a sensitivity of 37.5% and a specificity of 78.2%. ConclusionAcquisition of high-resolution histological images using the HS was feasible in clinical practice. Sensitivity and specificity were comparable to clinical routine. With more specific training and experience on image interpretation and acquisition, the HS may have the potential to enable more accuracy in the margin assessment of BCS specimens.

Does the Choice of Suture Materials Affect the Vas Deferens after Hernia Ring Closure in Inguinal Hernia Surgery: An Experimental Study in Rats

IntroductionLaparoscopic inguinal hernia repair is a commonly performed procedure in children. Currently, monofilament polypropylene and braided silk are the two most frequently used materials. Studies have suggested more tissue inflammatory reactions with the use of multifilament non-absorbable sutures. However, little is known about the effects of suture materials on adjacent vas deferens. The aim of this experiment was to compare the effect of non-absorbable monofilament and multifilament sutures on vas deferens in laparoscopic hernia repair. MethodsAll animal operations were performed by a single surgeon under aseptic conditions and anaesthesia. Ten male Sprague Dawley rats were divided into two groups. In Group I, “hernia repair” was performed using 5.0 Silk. In Group II, polypropylene sutures (Prolene®; Ethicon, Somerville, N.J., USA) were used. All animals also received sham operations in the left groin as a control. After 14 days, the animals were euthanised and a segment of vas deferens just adjacent to the suture was excised for histological review by an experienced pathologist who was blind to the treatment groups of the respective specimens. ResultsThe body sizes of the rats in each group were comparable. Group I had significantly smaller vas deferens than Group II (diameter: 0.2 vs. 0.6 ± 0.2, p = 0.005). Silk sutures appeared to cause more tissue adhesion than Prolene® sutures, as graded by blind assessors (adhesion grade: 2.8 ± 1.3 vs. 1.8 ± 0.8, p = 0.1), although this did not reach statistical significance. There was no significant difference in the histological fibrosis score and inflammation score. ConclusionThe only effect of non-absorbable sutures on vas deferens in this rat model was the reduced cross-sectional area of vas deferens and increased tissue adhesion when using silk sutures. However, there was no significant histological difference in inflammation or fibrosis caused by either material.

Holistic View on the Structure of Immune Response: Petri Net Model

The simulation of immune response is a challenging task because quantitative data are scarce. Quantitative theoretical models either focus on specific cell-cell interactions or have to make assumptions about parameters. The broad variation of, e.g., the dimensions and abundance between lymph nodes as well as between individual patients hampers conclusive quantitative modeling. No theoretical model has been established representing a consensus on the set of major cellular processes involved in the immune response. In this paper, we apply the Petri net formalism to construct a semi-quantitative mathematical model of the lymph nodes. The model covers the major cellular processes of immune response and fulfills the formal requirements of Petri net models. The intention is to develop a model taking into account the viewpoints of experienced pathologists and computer scientists in the field of systems biology. In order to verify formal requirements, we discuss invariant properties and apply the asynchronous firing rule of a place/transition net. Twenty-five transition invariants cover the model, and each is assigned to a functional mode of the immune response. In simulations, the Petri net model describes the dynamic modes of the immune response, its adaption to antigens, and its loss of memory.

P271 Variability in histologic activity of paired same-segment biopsies in Crohn’s disease dictates multiple biopsies needed to accurately evaluate histological status

Abstract Background At present, the significance of histologic remission in Crohn’s disease [CD] is unclear. One of the main reasons is the heterogeneous distribution of the lesions. This contrasts with ulcerative colitis, where histologic scoring systems have been applied with success on limited numbers of biopsies. We assessed the variability of histologic activity in paired same-segment biopsies from CD patients. Methods All CD patients undergoing ileocolonoscopy between January 2020 and July 2022 at our tertiary referral center were eligible for inclusion. Clinical data were retrospectively collected from medical records. Endoscopies were video-recorded and the Simple Endoscopic Score for Crohn’s Disease [SES-CD] was used to evaluate endoscopic activity. Paired biopsies were taken within the most affected area per segment, in accordance with ECCO guidelines. The Global Histology Activity Score (GHAS), the Geboes Score [GS], the Robart’s Histopathology Index [RHI] and the Nancy Histological Index [NHI] were determined for each biopsy by an experienced IBD pathologist [GDH] blinded to the endoscopic scores. The cut-off values for histologic remission were set at GHAS ≤4, GS &amp;lt;2B.1, RHI ≤3 and NI ≤1 (absence of neutrophils for RHI and NI). Variability was assessed by intraclass correlation coefficient [ICC] and Kappa statistics. Association between histologic and endoscopic scores was studied with Spearman’s rank correlation coefficient. Results A total of 151 biopsy pairs from 128 patients were analysed. Patient characteristics are summarised in Table 1. Endoscopic activity was observed in 79% of segments. In patients with endoscopic remission, histologic activity (presence of neutrophils) was found in 29% of cases. There was a moderate association between the endoscopic and histologic assessment, with correlation levels ranging from 0.48 to 0.52 [p&amp;lt;0.001]. Histologic indices showed fair agreement between paired biopsies [Table 2]. Looking at the predefined cut-offs, moderate variability across the different histological scores was seen [Table 3]. When comparing biopsies from segments with endoscopic activity and those without, overall higher variability in active segments and substantial agreement in paired biopsies from inactive segments was observed [Table 4]. Conclusion The patchy nature of microscopic inflammation in CD is reflected in the histologic scores of paired same-segment biopsies, with only moderate agreement across histologic scoring indices, although lower variability was seen in biopsies from endoscopic inactive segments. Endoscopic activity only moderately reflects histological status. Multiple biopsies per segment are therefore needed for accurate histological evaluation, the optimal number necessary remains to be explored.

Compatible-domain Transfer Learning for Breast Cancer Classification with Limited Annotated Data

Microscopic analysis of breast cancer images is the primary task in diagnosing cancer malignancy. Recent attempts to automate this task have employed deep learning models whose success has depended on large volumes of data, while acquiring annotated data in biomedical domains is time-consuming and may not always be feasible. A typical strategy to address this is to apply transfer learning using pre-trained models on a large natural image database (e.g., ImageNet) instead of training a model from scratch. This approach, however, has not been effective in several previous studies due to fundamental differences between natural and medical images. In this study, for the first time we proposed the idea of using a compatible data set of histopathological images to classify breast cancer cytological biopsy specimens. Despite intrinsic differences between histopathological and cytological images, we demonstrate that the features learned by deep networks during the pre-training procedure are compatible with those obtained throughout fine-tuning process. To thoroughly investigate this assertion, we explore three different strategies for training as well as two different approaches for fine-tuning deep learning models. By comparing the obtained results with those of previous state-of-the-art research conducted on the same data set, we demonstrate that the proposed method boasts of improved classification accuracy by 6% to 17% compared to the studies which were based on traditional machine learning techniques, and also enhanced accuracy by roughly 7% compared to those who utilized deep learning methods, eventually achieving 98.73% validation accuracy and 94.55% test accuracy. Exploring different training scenarios also revealed that using a compatible dataset has helped to elevate the classification accuracy by 3.0% compared to the typical approach of using ImageNet. Experimental results show that our approach, despite using a very small number of training images, has achieved performance comparable to that of experienced pathologists and has the potential to be applied in clinical settings.