Cervical cancer metastasis to the brain: A case report and review of literature

Background: Intracranial metastasis from cervical cancer is a rare occurrence. Methods: In this study we describe a case of cervical cancer metastasis to the brain and perform an extensive review of literature from 1956 to 2016, to characterize clearly the clinical presentation, treatment options, molecular markers, targeted therapies, and survival of patients with this condition. Results: An elderly woman with history of cervical cancer in remission, presented 2 years later with a right temporo-parietal tumor, which was treated with surgery and subsequent stereotactic radiosurgery (SRS) to the resection cavity. She then returned 5 months later with a second solitary right lesion; she again underwent surgery and SRS to the resection cavity with no signs of recurrence 6 months later. According to the reviewed literature, the most common clinical presentation included females with median age of 48 years; presenting symptoms such as headache, weakness/hemiplegia/hemiparesis, seizure, and altered mental status (AMS)/confusion; multiple lesions mostly supratentorially located; poorly differentiated squamous cell carcinoma; and additional recurrences at other sites. The best approach to treatment is a multimodal plan, consisting of SRS or whole brain radiation therapy (WBRT) for solitary brain metastases followed by chemotherapy for systemic disease, surgery and WBRT for solitary brain lesions without systemic disease, and SRS or WBRT followed by chemotherapy for palliative care. The overall prognosis is poor with a mean and median survival time from diagnosis of brain metastasis of 7 and 4.6 months, respectively. Conclusion: Future efforts through large prospective randomized trials are warranted to better describe the clinical presentation and identify more effective treatment plans

Artificial intelligence-based multi-class histopathologic classification of kidney neoplasms

Artificial intelligence (AI)-based techniques are increasingly being explored as an emerging ancillary technique for improving accuracy and reproducibility of histopathological diagnosis. Renal cell carcinoma (RCC) is a malignancy responsible for 2% of cancer deaths worldwide. Given that RCC is a heterogenous disease, accurate histopathological classification is essential to separate aggressive subtypes from indolent ones and benign mimickers. There are early promising results using AI for RCC classification to distinguish between 2 and 3 subtypes of RCC. However, it is not clear how an AI-based model designed for multiple subtypes of RCCs, and benign mimickers would perform which is a scenario closer to the real practice of pathology. A computational model was created using 252 whole slide images (WSI) (clear cell RCC: 56, papillary RCC: 81, chromophobe RCC: 51, clear cell papillary RCC: 39, and, metanephric adenoma: 6). 298,071 patches were used to develop the AI-based image classifier. 298,071 patches (350 × 350-pixel) were used to develop the AI-based image classifier. The model was applied to a secondary dataset and demonstrated that 47/55 (85%) WSIs were correctly classified. This computational model showed excellent results except to distinguish clear cell RCC from clear cell papillary RCC. Further validation using multi-institutional large datasets and prospective studies are needed to determine the potential to translation to clinical practice

A pyramidal deep learning pipeline for kidney whole-slide histology images classification

Renal cell carcinoma is the most common type of kidney cancer. There are several subtypes of renal cell carcinoma with distinct clinicopathologic features. Among the subtypes, clear cell renal cell carcinoma is the most common and tends to portend poor prognosis. In contrast, clear cell papillary renal cell carcinoma has an excellent prognosis. These two subtypes are primarily classified based on the histopathologic features. However, a subset of cases can a have a significant degree of histopathologic overlap. In cases with ambiguous histologic features, the correct diagnosis is dependent on the pathologist's experience and usage of immunohistochemistry. We propose a new method to address this diagnostic task based on a deep learning pipeline for automated classification. The model can detect tumor and non-tumoral portions of kidney and classify the tumor as either clear cell renal cell carcinoma or clear cell papillary renal cell carcinoma. Our framework consists of three convolutional neural networks and the whole slide images of kidney which were divided into patches of three different sizes for input into the networks. Our approach can provide patchwise and pixelwise classification. The kidney histology images consist of 64 whole slide images. Our framework results in an image map that classifies the slide image on the pixel-level. Furthermore, we applied generalized Gauss-Markov random field smoothing to maintain consistency in the map. Our approach classified the four classes accurately and surpassed other state-of-the-art methods, such as ResNet (pixel accuracy: 0.89 Resnet18, 0.92 proposed). We conclude that deep learning has the potential to augment the pathologist's capabilities by providing automated classification for histopathological images

Standardized Reporting of Microscopic Renal Tumor Margins: Introduction of the Renal Tumor Capsule Invasion Scoring System

Purpose Renal tumor enucleation allows for maximal parenchymal preservation. Identifying pseudocapsule integrity is critically important in nephron sparing surgery by enucleation. Tumor invasion into and through the capsule may have clinical implications, although it is not routinely commented on in standard pathological reporting. We describe a system to standardize the varying degrees of pseudocapsule invasion and identify predictors of invasion. Materials and Methods We performed a multicenter retrospective review between 2002 and 2014 at Indiana University Hospital and Loyola University Medical Center. A total of 327 tumors were evaluated following removal via radical nephrectomy, standard margin partial nephrectomy or enucleation partial nephrectomy. Pathologists scored tumors using our i-Cap (invasion of pseudocapsule) scoring system. Multivariate analysis was done to determine predictors of higher score tumors. Results Tumor characteristics were similar among surgical resection groups. Enucleated tumors tended to have thinner pseudocapsule rims but not higher i-Cap scores. Rates of complete capsular invasion, scored as i-Cap 3, were similar among the surgical techniques, comprising 22% of the overall cohort. Papillary histology along with increasing tumor grade was predictive of an i-Cap 3 score. Conclusions A capsule invasion scoring system is useful to classify renal cell carcinoma pseudocapsule integrity. i-Cap scores appear to be independent of surgical technique. Complete capsular invasion is most common in papillary and high grade tumors. Further work is warranted regarding the relevance of capsular invasion depth as it relates to the oncologic outcome for local recurrence and disease specific survival

Comparing oncologic outcomes in patients undergoing surgery for oncocytic neoplasms, conventional oncocytoma, and chromophobe renal cell carcinoma

Introduction Oncocytic neoplasms are renal tumors similar to oncocytoma, but their morphologic variations preclude definitive diagnosis. This somewhat confusing diagnosis can create treatment and surveillance challenges for the treating urologist. We hypothesize that these subtle morphologic variations do not drastically affect the malignant potential of these tumors, and we sought to demonstrate this by comparing clinical outcomes of oncocytic neoplasms to those of classic oncocytoma and chromophobe. Methods We gathered demographic and outcomes data for patients with variant oncocytic tumors. Oncologic surveillance was conducted per institutional protocol in accordance with NCCN guidelines. Descriptive statistics were used to compare incidence of metastasis and death against those for patients with oncocytoma and chromophobe. Three hundred and fifty-one patients were analyzed: 164 patients with oncocytoma, 28 with oncocytic neoplasms, and 159 with chromophobe tumors. Results Median follow-up time for the entire cohort was 32.4 months, (interquartile range 9.2–70.0). Seventeen total patients (17/351, 4.9%) died during the course of the study. In patients with oncocytoma or oncocytic neoplasm, none were known to metastasize or die of their disease. Only chromophobe tumors >6 cm in size in our series demonstrated metastatic progression and approximately half of these metastasized tumors demonstrated sarcomatoid changes. Conclusion Variant oncocytic neoplasms appear to have a natural course similar to classic oncocytoma. These tumors appear to have no metastatic potential, and oncologic surveillance may not be indicated after surgery

Spontaneous resolution of inflammatory myofibroblastic tumor of the kidney

Inflammatory myofibroblastic tumor (IMT) of the kidney is a rare and benign condition often confused with renal malignancy based on clinical presentation and radiologic evaluation that has commonly been treated with nephrectomy. Utilizing renal mass biopsy to help diagnose and guide therapeutic intervention is increasing but has not been universally adopted to this point. We present a case of an incidentally found atypical renal mass in a 71-year-old female diagnosed as inflammatory myofibroblastic tumor of the kidney after core needle biopsy. This tumor was managed conservatively without surgical intervention and resolved spontaneously

Biomarkers for Kidney-Transplant Rejection: A Short Review Study

Kidney transplantation is the preferred treatment for end-stage renal failure, but the limited availability of donors and the risk of immune rejection pose significant challenges. Early detection of acute renal rejection is a critical step to increasing the lifespan of the transplanted kidney. Investigating the clinical, genetic, and histopathological markers correlated to acute renal rejection, as well as finding noninvasive markers for early detection, is urgently needed. It is also crucial to identify which markers are associated with different types of acute renal rejection to manage treatment effectively. This short review summarizes recent studies that investigated various markers, including genomics, histopathology, and clinical markers, to differentiate between different types of acute kidney rejection. Our review identifies the markers that can aid in the early detection of acute renal rejection, potentially leading to better treatment and prognosis for renal-transplant patients

Diffuse Lewy Body Disease and Alzheimer Disease: Neuropathologic Phenotype Associated With the PSEN1 p.A396T Mutation

In sporadic and dominantly inherited Alzheimer disease (AD), aggregation of both tau and α-synuclein may occur in neurons. Aggregates of either protein occur separately or coexist in the same neuron. It is not known whether the coaggregation of tau and α-synuclein in dominantly inherited AD occurs in association with specific mutations of the APP, PSEN1, or PSEN2 genes. The aim of this study was to provide the first characterization of the neuropathologic phenotype associated with the PSEN1 p.A396T mutation in a man who was clinically diagnosed as having AD, but for whom the PSEN1 mutation was found postmortem. The proband, who was 56 years old when cognitive impairment first manifested, died at 67 years of age. Neuropathologically, 3 proteinopathies were present in the brain. Widespread α-synuclein-immunopositive neuronal inclusions suggested a diagnosis of diffuse Lewy body disease (DLBD), while severe and widespread tau and amyloid-β pathologies confirmed the clinical diagnosis of AD. Immunohistochemistry revealed the coexistence of tau and α-synuclein aggregates in the same neuron. Neuropathologic and molecular studies in brains of carriers of the PSEN1 p.A396T mutation or other PSEN1 or PSEN2 mutations associated with the coexistence of DLBD and AD are needed to clarify whether tau and α-synuclein proteinopathies occur independently or whether a relationship exists between α-synuclein and tau that might explain the mechanisms of coaggregation

A deep learning framework for automated classification of histopathological kidney whole-slide images

Background: Renal cell carcinoma is the most common type of malignant kidney tumor and is responsible for 14,830 deaths per year in the United States. Among the four most common subtypes of renal cell carcinoma, clear cell renal cell carcinoma has the worst prognosis and clear cell papillary renal cell carcinoma appears to have no malignant potential. Distinction between these two subtypes can be difficult due to morphologic overlap on examination of histopathological preparation stained with hematoxylin and eosin. Ancillary techniques, such as immunohistochemistry, can be helpful, but they are not universally available. We propose and evaluate a new deep learning framework for tumor classification tasks to distinguish clear cell renal cell carcinoma from papillary renal cell carcinoma. Methods: Our deep learning framework is composed of three convolutional neural networks. We divided whole-slide kidney images into patches with three different sizes where each network processes a specific patch size. Our framework provides patchwise and pixelwise classification. The histopathological kidney data is composed of 64 image slides that belong to 4 categories: fat, parenchyma, clear cell renal cell carcinoma, and clear cell papillary renal cell carcinoma. The final output of our framework is an image map where each pixel is classified into one class. To maintain consistency, we processed the map with Gauss-Markov random field smoothing. Results: Our framework succeeded in classifying the four classes and showed superior performance compared to well-established state-of-the-art methods (pixel accuracy: 0.89 ResNet18, 0.92 proposed). Conclusions: Deep learning techniques have a significant potential for cancer diagnosis