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Volvulus refers to torsion of a segment of the gastrointestinal tract, which often leads to bowel obstruction. Obstruction of the intestinal lumen and impairment of vascular perfusion occur when the degree of torsion exceeds 180 and 360 degrees, respectively. Visceral volvulus causing obstruction is a common presentation to the emergency department. Ascertaining the gastrointestinal level and grading the severity of the obstruction is paramount as each can be managed entirely differently. A spectrum from nasogastric or rectal tube decompression, to endoscopic decompression with or without fixation, and finally, surgical intervention are all at the surgeons disposal. Clinical and radiographic assessment of both the pathological process and the patient provide all the necessary detail for appropriate management of what are often a highly comorbid cohort of patients. This article will summarize current evidence for management of the most common sources of volvulus; gastric, small bowel, and colonic.

Diabetic retinopathy (DR) is one of the most common retinal diseases that cause preventable blindness in diabetic patients. The timely screening and grading of retinal images minimize the possibility of vision loss. However, manual screening of retinal images, for detecting micro lesions in the early stages of DR, is time-consuming. This paper proposes an ensemble of deep convolutional neural network (CNN) models for accurate detection and grading of DR using fundus images. Each input image is divided into four patches at the first stage and passed on to pre-trained CNN models (InceptionV3, Xception) for training. The relevant features in the shallow-dense layers of CNN models are utilized as prior knowledge. The integration of shallow and dense layer features helps the model learn the significant information of DR images. At the second stage, a classifier based on artificial neural network is trained using the fused probability vectors of four patches. The results of individual CNN models are combined to generate the final decision in the third stage. This ensemble approach of multi-stage deep learning model improves the overall classification accuracy of diabetic retinopathy grading. Among the five different classification schemes presented in this paper, multistage patch-based deep CNN (MPDCNN), in which local patch-based and holistic details of fundus image are concatenated, provides the best classification accuracy. This ensemble classifier exhibits 96.2 % classification accuracy with fivefold cross-validation.

BackgroundRenal cell carcinoma (RCC) comprises of a spectrum of clinico-pathologically distinct entities thereby making it difficult to accurately predict the clinical outcome. Though many predictive factors have been described in literature, tumor stage and nuclear grade have been established to consistently correlate with the tumor behaviour. However, tumors in the same stage have shown to behave differently. Similarly subjectivity and lack of reproducibility in nuclear grade mandates use of more objective parameters such as digital nuclear morphometry which could provide consistent and more reliable results in predicting prognosis. The study was conducted with the main objective of comparing the histological grade and the nuclear morphometric variables in RCC for predicting the clinical outcome.Material and methodsA total of 219 cases of renal tumors in adults were retrieved retrospectively from the archives of pathology department in Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow and their clinical, gross and microscopic features were noted. Nuclear grading was done in 181 cases of clear cell and papillary RCC of which computer-assisted morphometry for various nuclear parameters was done in 100 cases where a follow-up data of at least 3 years was available. Nuclear grade and morphometric parameters were correlated statistically with the clinical outcome of the patients.ResultsHistological nuclear grade did not show statistically significant correlation with progression free survival (PFS). Higher values of mean nuclear area, mean nuclear circumference, mean nuclear major diameter and mean nuclear minor diameter were significant predictors of PFS with a strong inverse correlation.ConclusionNuclear morphometry is a more reliable predictor of clinical outcome in patients of RCC when compared to histological grade and should be included in predictive model with other clinical and pathological parameters to accurately determine tumor behaviour.

In the clinical assessment of chronic rhinosinusitis with nasal polyps (CRSwNP), evaluation of symptoms, nasoendoscopy findings and sinus computed tomography (CT) scan staging is employed in the management plan. Although prior studies have been done to assess the correlation of the three modalities in CRSwNP, such evaluation in patients with eosinophilic and non-eosinophilic types of CRSwNP requires further investigation. The aim of this study was to correlate the sinus CT scan staging to symptom and endoscopic scores in both types of CRSwNP.Patients with CRSwNP were classified into 2 types, eosinophilic and non-eosinophilic. Both types were assessed by Sino-Nasal Outcome Test 22 (SNOT-22) symptom score, nasal endoscopy Lund Kennedy grading (LK) score and sinus CT Lund Mackay (LM) staging score. All three modalities were correlated.Forty-four patients were recruited for this study. There was significant correlation between sinus CT and symptom scores in the eosinophilic (r=.51, p=.031) and the non-eosinophilic (r=.76, p.001) types. There was no significant correlation between symptom and nasoendoscopic scores in the eosinophilic (r=.12, p=.641) and the non-eosinophilic (r=.22, p=.276) types. There was also no significant correlation between sinus CT and nasoendoscopic scores in the eosinophilic (r=.20, p=.418) and the non-eosinophilic (r=.14, p=.508) types.Sinus CT staging correlates well with the symptom score for both the eosinophilic and the non-eosinophilic types of CRSwNP. The good correlation suggests both modalities are a reliable guide for their evaluation and management planning.

Hepatocellular carcinoma (HCC) is a type of primary liver malignant tumor with a high recurrence rate and poor prognosis even undergoing resection or transplantation. Accurate discrimination of the histologic grades of HCC plays a critical role in the management and therapy of HCC patients. In this paper, we discuss a deep learning-based diagnostic model for HCC histologic grading with multimodal Magnetic Resonance Imaging (MRI) images to overcome the problem of limited well-annotated data and extract the discriminated fusion feature referring to the clinical diagnosis experience of radiologists. Accordingly, we propose a novel Multimodality-Contribution-Aware TripNet (MCAT) based on the metric learning and the attention-aware weighted multimodal fusion. The novelty of the method lies in the multimodality small-shot learning architecture designation and the multimodality adaptive weighted computing scheme. The comprehensive experiments are done on the clinic dataset with the well-annotation of lesion location by the professional radiologist. The experimental results show that our proposed MCAT is not only able to achieve acceptable quantitative measuring of HCC histologic grading based on the MRI sequences with small cases but also outperforms previous models in HCC histologic grading, reaching an accuracy of 84 percent, a sensitivity of 87 percent and precision of 89 percent.

Nuclear atypia scoring (NAS), forms a significant factor in determining individualized treatment plans and also for the prognosis of the disease. Automation of cancer grading using quantitative image-based analysis of histopathological images can circumvent the shortcomings of the prevailing manual grading and can assist the pathologists in cancer diagnosis. However, developing such a robust classifier model require sufficient amount of annotated data, while the labeled histopathological images are scarce and expensive to procure as annotation forms a time-consuming and laborious task. Hence, a semisupervised learning framework combined with the deep neural network based generative adversarial training, that can improve the performance of the classification model with limited annotated data, is proposed in this paper. The proposed NAS-SGAN model consists of discriminator and generator models that are trained in an adversarial manner using both labeled and unlabeled samples. The discriminator model is designed as an unsupervised model stacked over the supervised model sharing the model parameters and learns the data distribution by extracting the discriminative features. The generator model is trained over a stable feature matching objective function following a composite GAN architecture. The novelty of the proposed model is that, we have used a stacked supervised and unsupervised discriminator and a feature matching generator for the NAS-SGAN model and its for the first time the semi-supervised GAN model is explored for the grading of breast cancer. Experimental analysis shows that the proposed model could better discriminate different cancer grades thereby improving the robustness and accuracy of the system, even with limited amount of labeled samples.

PURPOSE To establish and validate an artificial intelligence (AI)-assisted automatic cataract grading program based on the Lens Opacities Classification System III (LOCSIII). SETTING Eye and Ear, Nose, and Throat (EENT) Hospital, Fudan University, Shanghai, China. DESIGN AI training. METHODS Advanced deep-learning algorithms, including Faster R-CNN and ResNet, were applied to the localization and analysis of the region of interest. An internal dataset from the EENT Hospital of Fudan University and an external dataset from the Pujiang Eye Study were used for AI training, validation, and testing. The datasets were automatically labeled on the AI platform in terms of the capture mode and cataract grading based on the LOCSIII system. RESULTS The AI program showed reliable capture mode recognition, grading, and referral capability for nuclear and cortical cataract grading. In the internal and external datasets, 99.4% and 100% of automatic nuclear grading, respectively, had an absolute prediction error of ≤ 1.0, with a satisfactory referral capability (area under the curve [AUC]: 0.983 for the internal dataset; 0.977 for the external dataset). 75.0% (internal dataset) and 93.5% (external dataset) of the automatic cortical grades had an absolute prediction error of ≤ 1.0, with AUCs of 0.855 and 0.795 for referral, respectively. Good consistency was observed between automatic and manual grading when both nuclear and cortical cataracts were evaluated. However, automatic grading of posterior subcapsular cataracts was impractical. CONCLUSIONS The AI program proposed in this study shows robust grading and diagnostic performance for both nuclear and cortical cataracts, based on LOCSIII.

The development of ultra-high-resolution CT (U-HRCT) is expected to improve the accuracy of coronary stenosis evaluation. This study aimed to evaluate the accuracy of the stenosis severities of coronary artery phantoms estimated using U-HRCT by comparing them to those estimated with conventional CT.Coronary artery phantoms with non-calcified and calcified lesions were scanned with conventional CT (64-row ​× ​0.625 ​mm) and U-HRCT (32-row ​× ​0.3125 ​mm). The coronary artery phantoms had lumen diameters of 2.0, 3.0, and 4.0 ​mm with non-calcified lesions representing 0%, 25%, 50%, and 75% stenosis and 3.0 and 4.0 ​mm with calcified lesions representing 0%, 25%, 50%, and 75% stenosis. The lumen diameters at the stenotic and non-stenotic regions were measured, and the stenosis severities were compared with the true values.For non-calcified lesions, conventional CT significantly underestimated the stenosis severity in the phantom showing 75% stenosis with lumen diameters of 2.0 and 3.0 ​mm (p ​ ​0.05), while the estimated stenosis severities were not significantly different from the true values at all settings with U-HRCT. For the calcified lesions, conventional CT overestimated the stenosis severities at all settings (p ​ ​0.05), while U-HRCT yielded estimations closer to the true values, although still with some overestimation (p ​ ​0.05).By using U-HRCT, the estimated stenosis severities of the coronary artery with non-calcified lesion become almost equal to the true value, while those with calcified lesion are still overestimated although they become closer to the true value.

High-grade endometrial carcinoma (HGEC) is a heterogeneous group of tumors with various morphologic, genetic, and clinical characteristics. Morphologically, HGEC includes high-grade endometrioid carcinoma, serous carcinoma, clear cell carcinoma, undifferentiated/dedifferentiated carcinoma, and carcinosarcoma. The morphologic classification has been used for prognostication and treatment decisions. However, patient management based on morphologic classification is limited by suboptimal interobserver reproducibility, variable clinical outcomes observed within the same histotype, and frequent discordant histotyping/grading between biopsy and hysterectomy specimens. Recent studies from The Cancer Genome Atlas (TCGA) Research Network established four distinct molecular subtypes: POLE-ultramutated, microsatellite unstable, copy number high, and copy number low groups. Compared to histotyping, the TCGA molecular classification appears superior in risk stratification. The best prognosis is seen in the POLE-ultramutated group and the worst in copy number high group, while the prognosis in the microsatellite unstable and copy number low groups is in between. The TCGA subtyping is more reproducible and shows a better concordance between endometrial biopsy and resection specimens. It has now become apparent that the molecular classification can supplement histotyping in patient management. This article provides an overview of the pathologic diagnosis/differential diagnosis of HGEC and the TCGA classification of endometrial cancers, with the clinical significance and applications of TCGA classification briefly discussed when appropriate.

Due to the wide variations in location, size, local invasiveness, and treatment options, the complications associated with surgery for giant cell tumor of bone have been sporadically reported. For quality assessment, fundamental data based on large-scale surveys of complications under a universal evaluation system is needed. The Dindo-Clavien classification is an evaluation system for complications based on severity and required intervention type and is suitable for the evaluation of surgery in a heterogeneous cohort.A multi-institutional retrospective survey of 141 patients who underwent surgery for giant cell tumor of bone in the extremity was performed. The incidence and risk factors of complications, type of intervention for complication control, and impact of complications on functional and oncological outcomes were analyzed using the Dindo-Clavien classification.Forty-six cases (32.6%) had one or more complications. Of them, 18 (12.8%), 11 (7.8%), and 17 (12.1%) cases were classified as Dindo-Clavien classification grade I, II, and III complications, respectively. There were no cases with grade IV or V complications. Progression in Campanacci grading (p = 0.04), resection (over curettage, p 0.0001), reconstruction with prosthesis (p = 0.0007), and prolonged operative duration (p = 0.0002) were significant risk factors for complications. Complications had a significant impact on function (p 0.0001). Differences in the impact of complication types and tumor location on function were confirmed. Complications had no impact on local recurrence and metastasis development.The Dindo-Clavien classification could provide fundamental information, under a uniform definition and classification system, on postoperative complications in patients with giant cell tumor of bone in terms of incidence, type of intervention for complication control, risk factors, and impact on functional outcome. The data are useful not only for preoperative evaluation for the risk of complications under specific conditions but also for quality assessment of surgery for giant cell tumor of bone.

Purpose: This review aimed to summarize the available literature on the clinical application of [18F] FLT PET imaging in primary brain tumours. Methods: A comprehensive search strategy based on Pubmed/Medline, Scopus, Web of Science, Cochrane Library, Google Scholar, and the Embase databases was carried on using the following search string: ('3` Fluorothymidine'/exp OR 'FLT' OR '[18F]-FLT' OR '[18F] Fluorothymidine') AND ('pet'/exp OR 'pet' OR 'positron emission tomography') AND ('glioma'/exp OR 'glioma' OR 'brain tumour'/exp OR 'brain tumour’). The search was updated till March 2021 and only articles in English and studies investigating the clinical applications of [18F] FLT PET and PET/CT in primary brain tumours were considered eligible for inclusion. Results: The literature search ultimately yielded 52 studies included in the systematic review, with main results as follows: a) the uptake of [18F] FLT may guide stereotactic biopsy but does not discriminate between grade II and III glioma. b) [18F] FLT uptake and texture parameters correlate with overall survival (OS) in newly diagnosed gliomas. c) In patients with recurrent glioma, proliferative volume (PV) and tumour-to-normal brain (T/N) uptake ratio are independent predictors of survival. d) Patients demonstrating response to therapy at [18F] FLT PET scan show longer OS compared to non-responders. e) [18F] FLT PET demonstrated good performance in discriminating tumour recurrence from radionecrosis. However, controversial results exist in comparative literature examining the performance of [18F] FLT vs. other radiotracers in the assessment of recurrence. Conclusion: [18F] FLT PET imaging has demonstrated potential benefits for grading, diagnostic and prognostic purposes, despite the small sample size studies due to the relatively low availability of the radiotracer.

BACKGROUND & AIMS: The assessment of therapeutic response after neoadjuvant treatment and pancreatectomy for pancreatic ductal adenocarcinoma (PDAC) has been an ongoing challenge. Several limitations have been encountered when employing current grading systems for residual tumor. Considering endoscopic ultrasound (EUS) represents a sensitive imaging technique for PDAC, differences in tumor size between preoperative EUS and postoperative pathology after neoadjuvant therapy were hypothesized to represent an improved marker of treatment response. METHODS: For 340 treatment-naïve and 365 neoadjuvant-treated PDACs, EUS and pathologic findings were analyzed and correlated with patient overall survival (OS). A separate group of 200 neoadjuvant-treated PDACs served as a validation cohort for further analysis. RESULTS: Among treatment-naïve PDACs, there was a moderate concordance between EUS imaging and postoperative pathology for tumor size (r = 0.726, P < .001) and AJCC 8(th) edition T-stage (r = 0.586, P < .001). In the setting of neoadjuvant therapy, a decrease in T-stage correlated with improved 3-year OS rates (50% vs 31%, P < .001). Through recursive partitioning, a cutoff of ≥47% tumor size reduction was also found to be associated with improved OS (67% vs 32%, P < .001). Improved OS using a ≥47% threshold was validated using a separate cohort of neoadjuvant-treated PDACs (72% vs 36%, P < .001). By multivariate analysis, a reduction in tumor size by ≥47% was an independent prognostic factor for improved OS (P = .007). CONCLUSIONS: The difference in tumor size between preoperative EUS imaging and postoperative pathology among neoadjuvant-treated PDAC patients is an important prognostic indicator and may guide subsequent chemotherapeutic management.

Contains fulltext : 251973.pdf (Publisher’s version ) (Closed access) Amidst the ongoing pandemic, the assessment of computed tomography (CT) images for COVID-19 presence can exceed the workload capacity of radiologists. Several studies addressed this issue by automating COVID-19 classification and grading from CT images with convolutional neural networks (CNNs). Many of these studies reported initial results of algorithms that were assembled from commonly used components. However, the choice of the components of these algorithms was often pragmatic rather than systematic and systems were not compared to each other across papers in a fair manner. We systematically investigated the effectiveness of using 3-D CNNs instead of 2-D CNNs for seven commonly used architectures, including DenseNet, Inception, and ResNet variants. For the architecture that performed best, we furthermore investigated the effect of initializing the network with pretrained weights, providing automatically computed lesion maps as additional network input, and predicting a continuous instead of a categorical output. A 3-D DenseNet-201 with these components achieved an area under the receiver operating characteristic curve of 0.930 on our test set of 105 CT scans and an AUC of 0.919 on a publicly available set of 742 CT scans, a substantial improvement in comparison with a previously published 2-D CNN. This article provides insights into the performance benefits of various components for COVID-19 classification and grading systems. We have created a challenge on grand-challenge.org to allow for a fair comparison between the results of this and future research.

Osteomas are slow-growing benign osseous tumors that particularly located in the paranasal sinuses (PS). Here, we aimed to define the clinical symptoms and features, diagnostic conditions caused by osteomas located in four different PSs, to evaluate the surgical indications and methods, to compare the factors that can affect the surgical decision, radiological findings, and prognosis.The data of patients with paranasal sinus lesions and diagnosed as osteoma according to the radiological imaging, who applied to our clinic between 2010 and 2020, were retrospectively collected and re-evaluated in the light of clinical, radiological, and pathological data. Patients who underwent surgical treatments and were definitively diagnosed as osteoma by pathology were enrolled in this study.We presented the data of 117 patients retrospectively. Most of these cases (n = 77, 65.8%) had an osteoma located in the frontal sinus, while 32 cases (27.4%) had osteoma in the ethmoid, two cases (1.7%) had in sphenoid and six cases (5.1%) had in maxillary sinus. We found that the presence of symptoms, diameter of osteoma, surgical indications including state of sinus drainage, and chronic/recurrent sinusitis influence the choices of physicians in management of frontal sinus osteomas. Moreover, we found a significant correlation between the grading systems defined to classify the frontal sinus osteoma to be operated (r = 0.878, 95% CI: 0.724-0.949, P0.0001).There are several grading systems useful for the choice of surgical approaches, however, the clinical symptoms and surgical indications should not be neglected in the follow-up of patients with frontal sinus osteoma. If there are concerns about the grade of osteoma and the endoscopic approach is considered not to be sufficient for resection, the surgical procedure may be initiated endoscopically, and, if necessary, it can be combined with an external approach.

The Hannover classification of vestibular schwannomas is designed to stratify tumors based on extrameatal extension and compression of the brainstem. We have previously reported the reliability of the Koos system, but to date, no study has assessed the reliability of the similar Hannover classification.We present an assessment of the intrarater and interrater reliability of the Hannover classification system.After institutional review board approval was obtained, a cross-sectional group of the magnetic resonance imaging of 40 patients with vestibular schwannomas varying in size comprised the study sample. Four raters were selected to assign a Hannover grade to 50 total scans. Interrater and intrarater reliability was calculated and reported using Fleiss's kappa, Kendall's W, and intraclass correlation coefficient (ICC).Interrater observer reliability was found to be moderate when measured using Fleiss' kappa (0.49), extremely strong using Kendall's W (0.93), and excellent as calculated by ICC (0.88). The results were all statistically significant (P0.05). Intrarater reliability for Hannover grade ranged from 0.77 to 1.00 (substantial to perfect). By Kendall's W and ICC, all raters had near perfect or excellent agreement. The results were all statistically significant (P0.05).The Hannover classification is a reliable system for grading the size of vestibular schwannomas. This supports its continued use and previously published results of the literature in studies relying on this classification. Further studies are needed to evaluate its validity and determine its role in preoperative counseling and determining microsurgery and radiosurgery outcomes.

In 2016, the World Health Organization Classification of CNS Tumors introduced molecular abnormalities that refined tumor diagnoses. Around this time, the introduction of large scale genetic mutational analyses quickly advanced our knowledge of recurrent abnormalities in disease. In 2017, the C-IMPACT group was established to render expert consensus opinions regarding the application of molecular findings into central nervous system tumor diagnoses. C-IMPACT have presented their recommendations in 7 peer-reviewed publications; this article details those recommendations that are expected to be incorporated into the upcoming fifth edition of the World Health Organization classification.

To investigate the performance of synthetic relaxometry, three-dimensional pseudo-continuous arterial spin labelling (pCASL) and diffusion-weighted imaging (DWI) in differentiating high-grade gliomas (HGGs) from low-grade gliomas (LGGs) and to compare with the conventional MRI.Seventy-two patients with gliomas (including 27 LGGs and 45 HGGs) were studied using synthetic magnetic resonance imaging (sy-MRI), pCASL, and DWI with a 3.0 T MR scanner. T1 relaxometry (T1), T2 relaxometry (T2), as well as proton density (PD) from sy-MRI, cerebral blood flow (CBF) from pCASL, apparent diffusion coefficient (ADC) from DWI and enhancement quality (EQ), proportion enhancing (PE) from conventional contrast enhanced image based Visually-Accessible-Rembrandt-Images (VASARI) scoring system, were all analyzed by two radiologists. The Student's t-test, Mann-Whitney U test or Fisher's exact test was used to compare the parameters between LGGs and HGGs. The diagnostic performance of each parameter and their combination for glioma grading were analyzed.Significant statistical differences in T1, PD, CBF, ADC, EQ and PE are observed between LGGs and HGGs (all P 0.001). The ADC values have higher discrimination abilities compared with other univariable parameters, with the AUC of 0.905. AUC values for conventional contrast-enhanced method, EQ and PE from VASARI, and conventional contrast-free method, CBF + ADC, are 0.873 and 0.912 respectively. The combined T1, PD, CBF and ADC model had the best performance for differentiating LGGs and HGGs with AUC, sensitivity and specificity of 0.993, 95.5%, 100%, respectively.Relaxometry parameters derived from synthetic MRI contributed to the discrimination of low-grade gliomas from high-grade gliomas. Proposed contrast-free approach combining T1, PD, CBF and ADC showed a strong discriminative power, and outperformed conventional approaches.

Exploiting a tissue diagnosis method to abstain the involuted operating and consume valuable reagents while realizing high-speed and inexpensive pathological grading technology to supply a better scheme for cancer therapy is a significant method of cancers detection. A promising immuno-fluorescence strategy was rationally designed and synthesized by loading ruthenium complex into cervical cancer-targeted DNA-cage, which was well used to realize high-speed and inexpensive diagnosis of clinical cervical cancer tumor tissues avoiding the traditional multi-stage process, thus demonstrating high application potential in clinical pathological grading and surgical judgment. Moreover, it has been finding that Apts-DNA@Ru can enrichment in the tumor region, interestingly, no enrichment in normal cervical cancer tissue. It has the potential to realize the integration of in vivo diagnose and further synchronous treatment in the near future. Thence, this study demonstrates a strategy for integration of cancer-targeted DNA-cage and fluorescent RuPOP as alternative IHC reagents for next-generation more rapid convenient cancer detection.

Ocular adnexal lymphoma accounts for approximately 1%-2% of non-Hodgkin lymphomas and 8% of extranodal lymphomas. Lacrimal gland lymphoma, especially bilateral lacrimal gland lymphoma, is rare, and imaging is often utilized to differentiate lacrimal lymphoma from other types of masses that involve the lacrimal gland and the fossa. We describe a 74-year-old male patient presenting with bilateral eye proptosis, lachrymose without pain, and no changes in eyesight. Brain-orbit magnetic resonance imaging revealed bilateral lacrimal fossa masses with regular contours, moderate enhancement, and restriction on diffusion imaging, consistent with a lymphoma diagnosis, which was confirmed by histopathological results. Positron emission tomography-computed tomography was used to determine lymphoma grading. MRI, especially diffusion imaging, can be useful for guiding clinicians in the diagnosis and differentiation of lacrimal gland lymphoma from other lesions.

We describe a procedure for classifying positively graded filiform nilpotent Lie algebras in low dimensions, over R , C , and Q . Using this procedure, we find all filiform nilpotent Lie algebras of dimension 15 and lower that admit a positive grading.

Points-based grading, though now traditional, faces powerful critiques: Such grading creates a low road to passing, it undermines motivation, it wastes time, and it causes stress. It creates an illusion of mathematical precision. It is unfriendly to necessary conditions for satisfactory performance. This paper defends the alternative of specifications grading. Specifications grading grades only on whether work meets a set of expectations for satisfactory performance, with the expectations set at a high but reachable level. With a high bar also comes opportunities to revise unsatisfactory work. I summarize arguments from the literature in support of the system, but I also give account of my two-year experiment in philosophy courses with specifications grading. Compared with points-based grading, specifications grading appears to motivate students better and helps them learn more. I consider objections from both traditionalists and so-called ‘ungraders.’ The former hope to secure the benefits of specifications grading while still keeping points. The latter favor eliminating grading altogether.

Mesothelial tumors are classified into benign or preinvasive tumors, and mesotheliomas. The benign or preinvasive group includes adenomatoid tumors, well-differentiated papillary mesothelial tumors, and mesothelioma in situ. Malignant tumors are mesotheliomas and can be localized or diffuse. Histological classification of invasive mesotheliomas into three major subtypes-epithelioid, sarcomatoid, and biphasic is prognostically important. It also plays a significant role in the treatment decisions of patients diagnosed with this deadly disease. Grading and subtyping of epithelioid mesotheliomas have been one of the major changes in the recent WHO classification of pleural tumors. Mesothelioma in situ has emerged as a precisely defined clinico-pathologic entity that for diagnosis requires demonstration of loss of BAP1 or MTAP by immunohistochemistry, or CDKN2A homozygous deletion by FISH. The use of these two biomarkers improves the diagnostic sensitivity of effusion specimens and limited tissue samples and is valuable in establishing the diagnosis of epithelioid mesothelioma. In this review, recent changes in the histologic classification of pleural mesothelioma, importance of ancillary diagnostic studies, and molecular characteristics of mesotheliomas are discussed.

Purpose This guideline provides evidence-based recommendations for the indications and technique-dose of external beam radiation therapy (EBRT) in hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (IHC). Methods The American Society for Radiation Oncology convened a task force to address 5 key questions focused on the indications, techniques, and outcomes of EBRT in HCC and IHC. It is intended to cover the definitive, consolidative, salvage, preoperative (including bridge to transplant), and adjuvant settings as well as palliative EBRT for symptomatic primary lesions. Recommendations were based on a systematic literature review and created using a predefined consensus-building methodology and system for grading evidence quality and recommendation strength. Results Strong recommendations are made for using EBRT as a potential first-line treatment in patients with liver-confined HCC who are not candidates for curative therapy, consolidative therapy after incomplete response to liver-directed therapies, and as a salvage option for local recurrences. The guideline conditionally recommends EBRT for patients with liver-confined multifocal or unresectable HCC, or those with macrovascular invasion, sequenced with systemic or catheter-based therapies. Palliative EBRT is conditionally recommended for symptomatic primary HCC and/or macrovascular tumor thrombi. EBRT is conditionally recommended as a bridge to transplant or prior to surgery in carefully selected patients. For patients with unresectable IHC, consolidative EBRT with or without chemotherapy should be considered, typically after systemic therapy. Adjuvant EBRT is conditionally recommended for resected IHC with high-risk features. Selection of dose-fractionation regimen and technique should be based on disease extent, disease location, underlying liver function, and available technologies. Conclusions The task force has proposed recommendations to inform best clinical practices on the use of EBRT for HCC and IHC with strong emphasis on multidisciplinary care. Future studies should focus on further defining the role of EBRT in the context of liver-directed and systemic therapies and refining optimal regimens and techniques.

Background/Aim. Heart transplantation is the most effective way to treat patients in the terminal stage of heart failure. Endomyocardial biopsy has proven to be a safe and appropriate technique, with little sampling error, and remains, to this day, one of the most commonly used methods for diagnosing acute rejection. In 1990, the International Society of Heart and Lung Transplantation defined a standardized system for grading the severity of acute transplant rejection regarding endomyocardial sampling histopathological analysis. The aim of the study was to assess the morphological, immunohistochemical, and immunofluorescent markers of cell- and antibody-mediated rejection of heart transplants in patients monitored during 2020. Methods. From 31 patients transplanted at the Clinic for Cardiac Surgery of the University Clinical Center of Serbia, endomyocardial biopsy material was obtained, then processed and analyzed at the Institute of Pathology of the Faculty of Medicine, University of Belgrade. Results. The average Transplant Rejection Score (TRS) value was 0.42. The Spearman's correlation test did not show a statistically significant relationship between the TRS value and the difference between the ejection fraction values three and twelve months after transplantation. Conclusion. The mean TRS value obtained in this study suggests dominant cell-mediated graft rejection.

Specific patterns of lung ultrasound (LUS) images are used to assess the severity of coronavirus disease 2019 (COVID-19) pneumonia, while such assessment is mainly based on clinicians' qualitative and subjective observations. In this study, we quantitatively analyze the LUS images to assess the severity of COVID-19 pneumonia by characterizing the patterns related to the pleural line (PL) and B-lines (BLs). Twenty-seven patients with COVID-19 pneumonia, including 13 moderate cases, seven severe cases, and seven critical cases, are enrolled. Features related to the PL, including the thickness (TPL) and roughness of the PL (RPL), and the mean (MPLI) and standard deviation (SDPLI) of the PL intensities are extracted from the LUS images. Features related to the BLs, including the number (NBL), accumulated width (AWBL), attenuation coefficient (ACBL), and accumulated intensity (AIBL) of BLs, are also extracted. The correlations of these features with the disease severity are evaluated. The performances of the binary severe/non-severe classification are assessed for each feature and support vector machine (SVM) classifiers with various combinations of features as input. Several features, including the RPL, NBL, AWBL, and AIBL, show significant correlations with disease severity (all ). The classification performance is optimal using the SVM classifier using all the features as input (area under the receiver operating characteristic (ROC) curve = 0.96, sensitivity = 0.93, and specificity = 1). These findings demonstrate that the proposed method may be a promising tool for automatic grading diagnosis and follow-up of patients with COVID-19 pneumonia.

Background With changes in T and N categories the 8th edition of the AJCC/UICC TNM staging system for pancreatic cancer resulted in improved prognostic staging, but inconsistencies were observed with specific stage groups. Tumour grading remains disregarded in prognostic staging. We aimed to validate the current staging system and to investigate the possibility of further optimization by integration of grading. Methods 1946 patients undergoing upfront surgical resection for pancreatic adenocarcinoma from 10/2001 to 12/2015 were identified from a prospective institutional database. Survival analyses based on the 8th UICC TNM edition were performed and rare TNM subgroups were reallocated based on survival. The impact of tumour grade on stage-specific survival was assessed and a TNMG staging system was developed. Results The 8th UICC staging system accurately stratified prognosis except for comparable survival in stages IB (pT2N0M0) and IIA (pT3N0M0). Regrouping of pT3N0M0 and pT1N1M0 to IB and of pT1N2M0 to II resulted in a modified staging system with higher consistency. High tumour grade (G3&G4 vs G1&G2) was associated with a significantly shorter survival in all new stage groups except for stage IV modified UICC. A TNMG-based prognostic stage grouping in which high tumour grade results in grouping with tumours of the next higher pTNM-stage resulted in improvement of prognostication in non-metastatic pancreatic cancer. Conclusions The 8th edition of the UICC TNM staging system leaves room for improvement. A TNMG staging system with adjustments in group-allocation of specific rarely occurring pTNM subgroups and integration of tumour grade results in improved prognostic stratification.

Diabetic Retinopathy (DR) is one of the most common diabetic diseases that affect the retina’s blood vessels. Too much of the glucose level in blood leads to blockage of blood vessels in the retina, weakening and damaging the retina. Automatic classification of diabetic retinopathy is a challenging task in medical research. This work proposes a Mixture of Ensemble Classifiers (MEC) to classify and grade diabetic retinopathy images using hierarchical features. We use an ensemble of classifiers such as support vector machine, random forest, and Adaboost classifiers that use the hierarchical feature maps obtained at every pooling layer of a convolutional neural network (CNN) for training. The feature maps are generated by applying the filters to the output of the previous layer. Lastly, we predict the class label or the grade for the given test diabetic retinopathy image by considering the class labels of all the ensembled classifiers. We have tested our approaches on the E-ophtha dataset for the classification task and the Messidor dataset for the grading task. We achieved an accuracy of 95.8% and 96.2% for the E-ophtha and Messidor datasets, respectively. A comparison among prominent convolutional neural network architectures and the proposed approach is provided.

The objective of this research is to develop a dehusked coconut grading machine for coconut cultivators and entrepreneurs. The basis of design is characterized by a tapered belt conveyor and sizing board with openings of increasing aperture which run along the edge of the belt conveyor. Dehusked coconuts are fed onto the tapered belt conveyor where the gravitational force pushes the nut towards the edge until it comes in contact with sizing board. Due to the tangential force, the nut reels along the sizing board, where dehusked coconuts are graded and allowed to fall through aperture according to their sizes. Performance tests indicated that the inclination angle and velocity of the belt significantly affected contamination ratio, grading efficiency and capacity at 5% significance level. The most efficient configuration for fully husked coconut and semi husked was a belt speed of 1.3 m/s with the inclination angle of 15° and belt speed of 1.5 m/s with the inclination angle of 15° respectively. The machine is acceptable by coconut growers and entrepreneurs.

Prostate cancer treatment planning is largely dependent upon examination of core-needle biopsies. The microscopic architecture of the prostate glands forms the basis for prognostic grading by pathologists. Interpretation of these convoluted three-dimensional (3D) glandular structures via visual inspection of a limited number of two-dimensional (2D) histology sections is often unreliable, which contributes to the under- and overtreatment of patients. To improve risk assessment and treatment decisions, we have developed a workflow for nondestructive 3D pathology and computational analysis of whole prostate biopsies labeled with a rapid and inexpensive fluorescent analogue of standard hematoxylin and eosin (H&E) staining. This analysis is based on interpretable glandular features and is facilitated by the development of image translation–assisted segmentation in 3D (ITAS3D). ITAS3D is a generalizable deep learning–based strategy that enables tissue microstructures to be volumetrically segmented in an annotation-free and objective (biomarker-based) manner without requiring immunolabeling. As a preliminary demonstration of the translational value of a computational 3D versus a computational 2D pathology approach, we imaged 300 ex vivo biopsies extracted from 50 archived radical prostatectomy specimens, of which, 118 biopsies contained cancer. The 3D glandular features in cancer biopsies were superior to corresponding 2D features for risk stratification of patients with low- to intermediate-risk prostate cancer based on their clinical biochemical recurrence outcomes. The results of this study support the use of computational 3D pathology for guiding the clinical management of prostate cancer. Significance: An end-to-end pipeline for deep learning–assisted computational 3D histology analysis of whole prostate biopsies shows that nondestructive 3D pathology has the potential to enable superior prognostic stratification of patients with prostate cancer.

Background: To classify intraoperative and postoperative complications using the modified Clavien classification system(MCCS) and modified Satava classification system(SCS) and to evaluate the para...

Background: Fistula-in-ano can be defined as a hollow tract or cavity which is lined by granulation tissue. In case of fistula-in-ano, one end of this fistula opens in the anal canal whereas the other end is located in perianal area. Fistula-in-ano can considerably affect quality of life of an individual because of perianal discharge of blood and pus. Imaging of these fistulas is an important part of management and MR imaging is important in assessing relationship between the fistulous tract and sphincter muscles. Moreover, MR imaging can reliably demonstrate transmural inflammation, secondary tracts/ramifications, and abscesses which cannot be diagnosed on the basis of conventional fistulography. Aims and Objectives: The aim of the study was to evaluate role of MRI in diagnosis and grading of perianal fistulae. Materials and Methods: This was a retrospective observational study, in which 60 patients with fistula-in-ano were included on the basis of a predefined inclusion and exclusion criteria. MR imaging of patients was done by 1.5 T MRI machine. Before MR imaging normal saline was injected in the fistulous tract from secondary/external opening, that is, opening around perianal area. Three plane images were obtained in all the cases. T1W, T2W, and STIR image sequences were obtained parallel to pelvic diaphragm. Coronal cuts were imaged parallel to anal canal. FAT suppressed T1W and T2W images in all cases. Type and grade of fistula were determined in all the cases. P < 0.05 was taken as statistically significant. Results: Out of total 60 patients, there were 46 (76.66%) males and 14 (23.33%) were females with a M:F ratio of 1:0.30. The mean age of male and female patients was found to be 41.93±8.96 years and 44.04±7.46 years, respectively. The most common type of fistula was found to be trans-sphincteric fistula which was seen in 31 (51.6%) cases followed by intersphencteric fistula 22 (36.6%). Extrasphincteric and suprasphincteric fistulae were relatively uncommon and were seen in 4 (6.66%) and 3 (5%) cases, respectively. MRI was accurate in diagnosis of the tract with position of internal opening and any abscess cavity or secondary tract in 23 patients. Therefore, the diagnostic accuracy of MRI was found to be 95.4%. Conclusion: MRI is an excellent tool in assessment of perianal fistula. It not only helps in precisely locating fistulous tract but also can demonstrate relationship between the fistulous tract and sphincter muscles. Moreover, it can very well demonstrate transmural inflammation, secondary tracts/ramifications, and abscesses which cannot be assessed by conventional fistulograms.

We report the sensitivity and specificity of van Herick (VH) grading in detecting gonioscopically-defined primary angle closure suspects (PACS) among subjects screened for participation in a population-based trial in southern China. A cut-off of VH≤25% was found to have sensitivity and specificity rates of 98.2% and 25.9%, respectively. Rates were adjusted for missing gonioscopy data, resulting in a sensitivity and specificity of 92.5% and 61.5%, respectively.

Background and Aims There are no agreed-on endoscopic signs for the diagnosis of villous atrophy (VA) in celiac disease (CD), necessitating biopsy sampling for diagnosis. Here we evaluated the role of near-focus narrow-band imaging (NF-NBI) for the assessment of villous architecture in suspected CD with the development and further validation of a novel NF-NBI classification. Methods Patients with a clinical indication for duodenal biopsy sampling were prospectively recruited. Six paired NF white-light endoscopy (NF-WLE) and NF-NBI images with matched duodenal biopsy sampling including the bulb were obtained from each patient. Histopathology grading used the Marsh-Oberhuber classification. A modified Delphi process was performed on 498 images and video recordings by 3 endoscopists to define NF-NBI classifiers, resulting in a 3-descriptor classification: villous shape, vascularity, and crypt phenotype. Thirteen blinded endoscopists (5 expert, 8 nonexpert) then undertook a short training module on the proposed classification and evaluated paired NF-WLE–NF-NBI images. Results One hundred consecutive patients were enrolled (97 completed the study; 66 women; mean age, 51.2 ± 17.3 years). Thirteen endoscopists evaluated 50 paired NF-WLE and NF-NBI images each (24 biopsy-proven VAs). Interobserver agreement among all validators for the diagnosis of villous morphology using the NF-NBI classification was substantial (κ = .71) and moderate (κ = .46) with NF-WLE. Substantial agreement was observed between all 3 NF-NBI classification descriptors and histology (weighted κ = 0.72-.75) compared with NF-WLE to histology (κ = .34). A higher degree of confidence using NF-NBI was observed when assessing the duodenal bulb. Conclusions We developed and validated a novel NF-NBI classification to reliably diagnose VA in suspected CD. There was utility for expert and nonexpert endoscopists alike, using readily available equipment and requiring minimal training. (Clinical trial registration number: NCT04349904.)

Central nervous system (CNS) tumours refer to tumours that occur in the tissues of the brain and/or spinal cord. These tumours arise as a result of abnormal growth of cells and may begin in different parts of the brain or spinal cord. There are many types of CNS tumours, which are further divided into subtypes. Despite decades of research conducted, CNS tumours remain among the deadliest of all cancers. It is most often challenging to treat these tumours, due to the risks involved, and biological characteristics associated with them. The classification, grading, and characterisation of CNS tumour plays a pivotal role in the management thereof. The current review provides an overview of CNS tumours, classification, grading and treatment, as well as their characterisation with specific focus on gliomas, ependymomas, oligodendrogliomas, meningiomas, medulloblastomas, schwannomas, gangliogliomas, and craniopharyngiomas. Doi: 10.28991/SciMedJ-2021-0304-8 Full Text: PDF

Chondrosarcomas are heterogeneous matrix-producing cartilaginous neoplasms with variable clinical behavior. Subtypes include conventional (75%), dedifferentiated (10%), clear cell (2%), mesenchymal (2%), and periosteal chondrosarcoma (

Breast cancer normally occurs in the breast cells of both men and women, but is prominent in women. Computer aided detection increases the chance of early detection and diagnosis. This paper proposes a breast cancer detection method using Nuclear Atypia Scoring (NAS). The proposed cancer detection method works by converting each and every cancerous tissue into objects. Along with detecting the grade, proposed mechanism gives the count of the detected cells. This assists pathologists in identifying whether cells are cancerous or not along with the count of each type. Proposed model was evaluated on MITOS-ATYPIA-14 Challenge dataset. Accuracy of 0.89 and precision of 0.87 is obtained by the best method. Results indicate that the proposed machine learning technique has better performance as compared to existing methods and can aid pathologists in the detection process.

Objective To observe the ultrasonographic features of arthritis in patients with primary Sjogren's syndrome (pSS) and to analyze its correlation with clinical manifestations and disease activity. Methods Ultrasound (US) examinations were performed in a total of 1200 joints of 40 patients with pSS. A semi-quantitative grading method (0–3) for scoring synovial hyperplasia, PD synovitis, bone erosion, tenosynovitis was used. The clinical and laboratory data were collected, disease activity was assessed. The correlation between US lesions and disease activity assessment and clinical manifestations was analyzed. Results US findings of musculoskeletal in patients with pSS mainly involved the small joints of the hands and wrists and the lesions were mild. The semi-quantitative score of musculoskeletal US was positively correlated with ESSPRI. The occurrence of musculoskeletal US lesions is associated with immunological abnormalities and inflammatory markers, and patients with high IgG, RF, and inflammatory markers are prone to abnormal US findings. Conclusion The incidence of arthritis in patients with pSS is high, and musculoskeletal US has its characteristics. The musculoskeletal US semi-quantitative method can effectively evaluate arthritis in patients with pSS, and the US score of arthritis has a certain correlation with the overall disease activity. US can provide a reference for the diagnosis of arthritis and disease activity assessment in patients with pSS.

Early diagnosis of breast cancer, the most common disease among women around the world, increases the chance of treatment and is highly important. Nuclear atypia grading in histopathological images plays an important role in the final diagnosis and grading of breast cancer. Grading images by pathologists is a time consuming and subjective task. Therefore, the existence of a computer‐aided system for nuclear atypia grading is very useful and necessary. In this study, two automatic systems for grading nuclear atypia in breast cancer histopathological images based on deep learning methods are proposed. A patch‐based approach is introduced due to the large size of the histopathological images and restriction of the training data. In the proposed system I, the most important patches in the image are detected first and then a three‐hidden‐layer convolutional neural network (CNN) is designed and trained for feature extraction and to classify the patches individually. The proposed system II is based on a combination of the CNN for feature extraction and a two‐layer Long short‐term memory (LSTM) network for classification. The LSTM network is utilised to consider all patches of an image simultaneously for image grading. The simulation results show the efficiency of the proposed systems for automatic nuclear atypia grading and outperform the current related studies in the literature.

To assess the diagnostic accuracy of conventional MRI for detecting and grading subscapularis (SSC) tears by applying the Yoo and Rhee classification. A total of 179 patients who underwent MRI followed by arthroscopic rotator cuff surgery were enrolled. Two musculoskeletal radiologists evaluated the SSC using axial, oblique sagittal, and oblique coronal MRI according to the Yoo and Rhee classification. Using arthroscopic findings as the reference standard, the sensitivity, specificity, accuracy, and interobserver agreement of SSC tears were analyzed. Arthroscopy confirmed that the numbers of type I, IIA, IIB, III, IV, and V tears were 35, 70, 35, 9, 9, and 0, respectively. The sensitivity, specificity, and accuracy of readers 1 and 2 for the detection of tears (type IIA or higher) were 85%, 75%, and 82%, and 89%, 70%, and 83%, respectively, while those for the detection of surgical candidates (type IIB or higher) were 77%, 75%, and 75%, and 77%, 83%, and 82%, respectively. The interobserver agreement for detecting SSC tear presence was substantial (κ = 0.70) for reader 1 vs. reader 2, and those for detecting the surgical candidate group was substantial (κ = 0.68) for reader 1 vs. reader 2. The interobserver agreement for grading SSC tears was excellent (κ = 0.86) for reader 1 vs. reader 2. Conventional MRI showed 82.5% and 78.5% average accuracy in detecting IIA and IIB or higher tears by applying the Yoo and Rhee classification for the diagnosis of SSC tears with an excellent interobserver agreement in tear grading.

PURPOSE Oral mucositis is a common complication in patients undergoing hematopoietic stem cell transplantation. Accurate oral mucositis grading is essential for both clinical practice and oral mucositis research. This study aimed to evaluate the accuracy of daily oral mucositis grading by nurses in a tertiary hospital in Australia. METHODS A retrospective study was undertaken to review the daily patient oral assessment record, including diet, pain, erythema, ulceration and the oral mucositis grade based on World Health Organization (WHO) oral mucositis grading scale. The accuracy of the grade was determined by the observations recorded, and reasons for inaccuracy were documented. Any repetition of the same error in the same patient was noted. RESULTS In total, 6841 oral assessments in 373 patients, conducted between 2017 and 2020, were reviewed. A total of 70% (N = 4781) were graded correctly. Of these, 64% (N = 3043) were grade 0. When the grade 0 scores were excluded, the accuracy of grading was reduced to 46% (N = 1738). Common reasons for incorrect grading included: unable to grade due to diet not specified, no ulceration and no pain was scored grade 1, no ulceration was scored as grade 2-4, oral intake was not taken into account, and pain without ulcer was scored 0. A total of 77% of the errors were repeated in the same patient on consecutive days. CONCLUSIONS Our results suggest there is frequent inaccurate evaluation of oral mucositis and a need for nurse training to accurately assess oral mucositis.

Placenta Accreta Spectrum (PAS) disorders have become a major iatrogenic obstetric complication worldwide. Data on the accuracy of ultrasound examination diagnosis are limited by incomplete confirmation and variability in the description of the different grades of PAS at delivery. The aim of this study was to compare our prenatal routine sonographic screening and diagnostic scoring system with a standardized clinical grading system at birth in patient at risk of PAS.This is a retrospective cohort study of 607 pregnant patients with at least one prior cesarean delivery between December 2013 and December 2018. All patients were assessed for PAS using our institutional prenatal sonographic scoring system and the corresponding ultrasound findings were compared with those of a standardized clinical intra-operative macroscopic grading system of the degree of accreta placentation at vaginal birth or laparotomy.PAS was diagnosed clinically at birth in 50 (8.2%) cases, 17 of which were confirmed by histopathology. A low (score ≤ 5), medium (score 6-7), high (score ≥ 8) probability for PAS was reported in 502, 61 and 44 cases, respectively. The probability score increased significantly (Both ends of the spectrum of accreta placentation remain difficult to diagnose antenatal and clinically at birth, in particular when no histopathologic confirmation is available. There is a need to develop ultrasound accuracy score systems that can differentiate between the different grades of PAS and which are validated by standardized clinical and pathology protocols.

Histologic grading using the Fédération Nationale des Centres de Lutte Contre Le Cancer (FNCLCC) system is not universally accepted as applicable to malignant peripheral nerve sheath tumor (MPNST), as its prognostic value is not well established.We retrospectively evaluated 99 cases of MPNST to investigate any association between the outcomes overall survival (OS) and progression-free survival (PFS), and predictor variables FNCLCC grade, clinical setting, tumor location, and tumor size at diagnosis using multivariable Cox proportional hazard analysis.Univariable and multivariable analysis demonstrate a statistically significant association between FNCLCC grade and both OS and PFS when comparing tumors by histologic grade. Of note, no deaths were observed in patients with grade 1 MPNST. Other variables associated with unfavorable outcomes include fragmented resection and primary site, with tumors in the extremities having favorable OS, but not PFS, when compared with those in truncal locations. Tumors in the head and neck had favorable PFS, but not OS, compared with those in the trunk. No statistically significant differences in OS or PFS were observed when comparing patient age and sex, tumor size at diagnosis, clinical setting (primary vs. type-1 neurofibromatosis vs. radiation associated) or history of neoadjuvant therapy. Interobserver agreement for FNCLCC grading of these tumors was considered good (S*=0.77, 95% confidence interval: 0.71-0.84).Association between FNCLCC grading and survival outcomes in MPNST suggests potential value to routinely grading these neoplasms. However, the subjectivity of the grading system, particularly when assigning a tumor differentiation score, may pose a challenge, especially in low and intermediate grade lesions.

Background We aimed to determine whether the histopathological grading of dysplastic nevi is an objective endeavour, considering interobserver variability, according to 2018 World Health Organization (WHO) Criteria. Methods In total, 179 cases of dysplastic nevi, with high and moderate degree of atypia, diagnosed and graded according to the previous criteria were reviewed by three pathologists. Then the observers graded the dysplastic nevi as low or high according to 2018 WHO criteria. Results Grading of dysplastic nevi was in complete agreement in 99 of 179 cases across three observers with a fair level of overall interobserver agreement (multirater Kfree: 0.40). The observers showed moderate to good agreement for most of the architectural features, except for criteria regarding focal continuous basal proliferation of melanocytes, density of non- nested junctional melanocytes and presence of dyscohesive nests of intraepidermal melanocytes. Whereas fair agreement was achieved for the cytological criteria. Conclusions The 2018 WHO criteria for dysplastic nevus will ensure a common approach to diagnosis and grading of dysplastic nevi. However, histopathological criteria, such as cytological features and focal continuous basal proliferation of melanocytes, should be improved, so as to ensure a more accurate surgical approach and risk assessment. This article is protected by copyright. All rights reserved.

Purpose To investigate the concordance of biopsy and pathologic International Society of Urological Pathology (ISUP) grading in salvage radical prostatectomy (SRP) patients for recurrent prostate cancer. Methods Within a high-volume center database, we identified patients who underwent SRP for recurrent prostate cancer (PCa) between 2004 and 2020. Upgrading, downgrading, concordance, and any discordance between posttreatment biopsy ISUP and ISUP at SRP were tested. Logistic regression models were used to predict ISUP upgrading and ISUP discordance. Models were adjusted for prostatic specific antigen before SRP, age at surgery, initial prostatic specific antigen (PSA), type of primary treatment, time from primary PCa diagnosis to SRP, number of positive cores at biopsy, and original Gleason score. Results Overall, 184 patients with available biopsy and pathologic ISUP grading were identified. Of those, 17.4% (n = 32), 40.8% (n = 75), 19.6% (n = 36), and 22.2% (n = 41) harbored biopsy ISUP 1, ISUP 2, ISUP 3, and ISUP 4-5 grading, respectively. Pathologic ISUP 1, ISUP 2, ISUP 3, and ISUP 4-5 grading was recorded in 6.0% (n = 11), 40.8% (n = 75), 32.1% (n = 59), and 21.2% (n = 39), respectively. Median PSA before SRP was 5.5 ng/ml (interquartile range [IQR]: 3.1-8.1 ng/ml), median age at SRP was 65.1 years (IQR:60.7-69.4 years) and median time from original PCa diagnosis to SRP was 47 months (IQR: 27.3-85.2 months). Concordance of biopsy and pathologic ISUP was identified in 45.1% (n = 83). Conversely, any ISUP discordance, upgrading and downgrading of at least one ISUP group was identified in 54.9% (n = 101), 35.3% (n = 65), and 19.6% (n = 36). In logistic models, none of the preoperative characteristics was associated with upgrading or ISUP discordance (all p > 0.1). Conclusion Discordance between biopsy and pathologic ISUP grading is common at SRP. However, in 45% of SRP cases biopsy ISUP is capable to predict pathologic ISUP. Further studies are necessary to identify characteristics for ISUP upgrading at SRP.

Patients with peritoneal dissemination (PD) caused by abdominal malignancies are often associated with massive ascites, which shows extremely dismal prognosis because of the discontinuation of systemic chemotherapy mostly due to poor performance status. Many treatment methods, such as simple drainage, peritoneovenous shunting (PVS) and cell-free and concentrated reinfusion therapy (CART), have been used for symptom relief. However, the clinical efficacies of these methods have not been fully investigated yet. Recently, we developed the Clinical Practice Guideline for PD caused by various malignancies according to "Minds Clinical Practice Guideline Development Guide 2017". In this guideline, we systematically reviewed information on clinical diagnosis and treatments for PD using PubMed databases (2000 – 2020), and clarified the degree of recommendation for clinical questions (CQ). The evidence level was divided into groups by study design and quality. The literature level and a body of evidence were evaluated in reference to the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system. Based on the results of systematic review, the strength of the recommendations was evaluated at a consensus meeting of the Guideline Committee. This is the English synopsis of the part of treatment of malignant ascites in Clinical Practice Guideline for PD, 2021 in Japanese. The guidelines summarize the general aspect of the treatment of malignant ascites and statements with recommendation strengths, evidence levels, agreement rates and future perspective for four raised clinical questions.

Objective: To determine the relationship of severity of thrombocytopenia with various grades of esophageal varices in patients with cirrhosis. Study design: A cross sectional observational study. Place and duration of study: Medicine department of Pakistan Railway General Hospital from 1st September, 2018 to 31st August, 2020. Material and Methods: The record of 100 patients with cirrhosis having concomitant thrombocytopenia and esophageal varices was retrospectively analyzed. The information about clinical, hematological, biochemical, ultrasound and endoscopic findings was retrieved from medical record. On the basis of platelet count, four groups were made. Group I consisted of patients with a platelet count ≤ 20,000/ µl, group II 21,000-49,000/ µl, group III 50,000-99,000/ µl, and group IV 100,000- 149,000/ µl. Esophageal varices were reclassified as small and large varices group depending on the size. Correlation of thrombocytopenia with grading of esophageal varices was calculated using spearman’s correlation. Results: Out of 100 patients, 76% had large varices and 24% had small varices. Thrombocytopenia was more severe in patients with large varices group when compared with small varices group. There was significant negative correlation between thrombocytopenia and grading of esophageal varices (r= -.691; P < 0.001). Conclusion: The severity of thrombocytopenia increased with increase in the size of esophageal varices. Low platelet count can strongly predict large varices in patients with cirrhosis.

Background Risk assessment on the molecular level is important in predictive pathology to determine the risk of metastatic disease for ERpos, HER2neg breast cancer. The gene expression test EndoPredict (EP) was trained and validated for prediction of a 10-year risk of distant recurrence to support therapy decisions regarding endocrine therapy alone or in combination with chemotherapy. The EP test provides the 12-gene Molecular Score (MS) and the EPclin-Score (EPclin), which combines the molecular score with tumor size and nodal status. In this project we investigated the correlation of 12-gene MS and EPclin scores with classical pathological markers. Methods EndoPredict-based gene expression profiling was performed prospectively in a total of 1652 patients between 2017 and 2020. We investigated tumor grading and Ki67 cut-offs of 20% for binary classification as well as 10% and 30% for three classes (low, intermediate, high), based on national and international guidelines. Results 410 (24.8%) of 1652 patients were classified as 12-gene MS low risk and 626 (37.9%) as EPclin low risk. We found significant positive associations between 12-gene MS and grading (p p = 0.001), 12-gene MS and Ki67 (p p 20% were classified as low risk by EPclin. Same differences were seen comparing EP test results and tumor grading. Conclusion In this study we could show that EP risk scores are distributed differentially among Ki67 expression groups, especially in Ki67 low and high tumors with a substantial proportion of patients with EPclin high risk results in Ki67 low tumors and vice versa. This suggests that classical pathological parameters and gene expression parameters are not interchangeable, but should be used in combination for risk assessment.

Context: An “Intra-cranial space occupying lesion” (ICSOL) is defined as a mass in the cranial cavity with a diverse etiology like benign or malignant neoplasm, inflammatory or parasitic lesion, haematoma or arterio-venous malformation. Aims: The aim was to study the histomorphological spectrum of intracranial space occupying lesions at a tertiary care centre hospital in order to give accurate pretherapeutic diagnosis to facilitate better therapeutic results. Setting and Design: This prospective and descriptive type of study of two years duration was carried out from November 2017 to October 2019 in Govt. Medical College and Superspeciality Hospital, Nagpur in the Department of Pathology. A total of 300 samples were received from Department of Neurosurgery. Materials and Methods: Detailed collection of clinical data was done in all patients regarding age, sex, clinical symptoms and radiological findings. The samples were received in 10% formalin and subjected to routine histopathological processing. Slides prepared were stained with hematoxylin and eosin stain. Special stains were done wherever required. Results: Total 300 cases were studied of which 261 cases (87%) were neoplastic in nature and 39 cases (13%) were non neoplastic. Amongst primary tumours, most common were gliomas (48%) followed by meningiomas (20%) and schwannomas (9%). Conclusion: ICSOLs can present with nonspecific clinical features. Radio diagnostic investigations are helpful adjunct tools but cannot give exact diagnosis. Histopathological examination remains a gold standard for diagnosing and grading of tumours on which basis clinician can decide further line of management. Keywords: Histomorphology, ICSOL, Glioma, Meningioma.

Grading is a common practice in general education settings. Few teachers, however, are clear about how they should grade students with significant cognitive disabilities when those students are included in the general education classroom. Unfortunately, existing research provides minimal information to aid teachers and education leaders as they consider fair and equitable grading practices for these students. In this article, we examine the four criteria for high-quality inclusive grading outlined by The William & Mary Training & Technical Assistance Center for grading within the inclusive classroom: (a) grades are accurate, (b) grades are meaningful, (c) grades are consistent, and (d) grades are supportive of learning and discuss how these criteria could be applied to provide a more fair and equitable grading system for students with significant cognitive disabilities.