Treatment response and recurrence of conjunctival melanoma with orbital invasion treated with immune checkpoint inhibitors: case report and literature review.

INTRODUCTION: Conjunctival melanoma (CM) has genetic characteristics that are similar to primary cutaneous melanoma (PCM). The management of advanced CM with orbital metastasis was limited until the adoption of novel immunotherapy agents that significantly improved the survival of metastatic PCM. PURPOSE: To review and compare the immune checkpoint inhibitor (ICI) treatment response in cases reported in the English literature with orbital involvement secondary to CM versus PCM. In addition, we report a case of local recurrence of CM in a young female after successful treatment with ICI. METHODS: In addition to reviewing the chart of one patient who presented to our clinic, we conducted a comprehensive literature review to identify CM cases and cases with orbital metastasis secondary to advanced CM and PCM. Outcomes included patient demographics, response to ICI, and associated adverse effects. RESULTS: There were ten cases with orbital involvement, four were secondary to CM, and six were metastasis from PCM. Orbital metastasis from PCM regressed following treatment with ICI agents, whereas those secondary to CM resolved completely. There were 19 cases of CM without orbital invasion. Of the 29 cases identified, complete resolution of ocular melanoma was achieved in 15 patients, representing 52% of the cases collectively, and none of them reported recurrence except in our case. CONCLUSION: CM with orbital invasion responds well to ICIs, with manageable toxic effects. Despite the complete resolution, close observation is needed as the recurrence risk remains.

PD-L1 is highly expressed in ovarian cancer and associated with cancer stem cells populations expressing CD44 and other stem cell markers.

BACKGROUND: Immune checkpoint inhibitors, including PD-L1 (programmed death ligand-1) inhibitors have well documented anticancer therapeutic effect in most types of cancers but its use in the treatment of ovarian cancer is not yet proven. The aim of our study is to explore the predictive biomarkers in ovarian cancer and its association with the outcomes. We have investigated the role of PD-L1 expressions in the tumor microenvironment cells including immune cells and cancer stem cells in different types of ovarian cancer. METHODS: A total of 119 surgical archived ovarian cancer samples were collected from the pathology department at King Fahad Specialist Hospital, Dammam, Saudi Arabia that included serous carcinomas, clear cell carcinomas, mucinous carcinomas, endometrioid carcinomas, and granulosa cell tumors. Immunohistochemistry (IHC) staining was performed using (i) PD-L1 antibodies to detect PD-L1 expressions; (ii) CD8 and CD4 to detect Tumor Infiltrating Lymphocytes (TILs); and (iii) CD44, LGR5, and ALDH2 to detect stem cell markers. The clinicopathological data were collected from patients' medical record to investigate the association with PD-L1, TILs, and stem cells expressions. RESULTS: We report high PD-L1 expressions in 47.8% of ovarian cancer samples. PD-L1 expressions were detected in different types of epithelial ovarian cancer and were not associated with poor prognosis of ovarian cancer. However, determining the expression levels of TILs in the ovarian cancer tissues found that 81% (n = 97) of ovarian cancer samples have TILs that express both of CD8 and CD4 and significantly associated with high PD-L1 expressions. Interestingly, we have found that ovarian cancer tissues with high expressions of PD-L1 were associated with high expressions of stem cells expressing CD44 and LGR5. CONCLUSIONS: PD-L1 is highly expressed in the serous type of ovarian carcinomas and the overall expression of PD-L1 is not associated with poor survival rate. Furthermore, PD-L1 expressions are strongly associated with TILs and stem cell markers in ovarian cancer. Inhibiting the PD-L1 using immune checkpoint inhibitors might downregulate stem cell population that known to be associated with cancer recurrence.

The clinical significance of routine risk categorization in metastatic renal cell carcinoma and its impact on treatment decision-making: a systematic review.

Aim: To analyze responses to first-line metastatic renal cell carcinoma (mRCC) treatment stratified by risk criteria. Patients & methods: Clinical trials and observational studies of patients aged ≥18 years, published January 2005-May 2019, were identified via Ovid from MEDLINE, EMBASE, the Cochrane Central Trials Register and the Cochrane Database of Systematic Reviews. Data extracted included progression-free survival (PFS), overall survival (OS) and objective response rate (ORR). Results: 47/1269 articles met eligibility criteria. Most studies stratified patients by International Metastatic RCC Database Consortium (n = 19) or Memorial Sloan Kettering Cancer Center (n = 21). PFS, OS and ORR varied according to risk group. Conclusion: Pembrolizumab + axitinib, ipilimumab + nivolumab and avelumab + axitinib were most effective across all risk groups. Favorable-risk patients benefit from sunitinib treatment.

Update on the Treatment of Metastatic Urothelial Carcinoma.

Platinum-based combination chemotherapy has been the standard of care in the first-line treatment of metastatic urothelial carcinoma (mUC). Treatment of metastatic disease following progression on platinum-based regimens has evolved significantly in the last few years. Clinical trials are currently ongoing to determine how best to use and sequence these treatments. In this minireview, we will review current first-line treatment options in both cisplatin fit and cisplatin unfit patients and advances in first- and second-line treatments including chemotherapy and immunotherapy. This review reports key findings from the clinical trials especially highlighting the importance of PD-1 and PD-L1 inhibitors in the treatment of bladder/urothelial carcinomas.

Multifocal calcifying fibrous tumor at six sites in one patient: a case report.

Calcifying fibrous tumors (CFT) are rare benign tumors. They usually affect children and young adults and the incidence is equal in males and females. The usual clinical presentation is that of a painless mass. A computed tomography scan typically reveals a well-demarcated calcified lesion. CFT usually presents as a solitary mass and the commonest sites of occurrence are in soft tissues, the pleura, or the peritoneum. Multifocal occurrences at the same site have also been reported. The first case of CFT was reported in 1988. We present a rare case of multiple calcifying fibrous tumors at multiple sites in the same patient. To the best of our knowledge, this is the first ever reported case of multifocal CFT atsix different anatomical sites in one patient.

Correction: Gastrointestinal tract disorders classification using ensemble of InceptionNet and proposed GITNet based deep feature with ant colony optimization.

[This corrects the article DOI: 10.1371/journal.pone.0292601.].

White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning.

White blood cells (WBCs) are an indispensable constituent of the immune system. Efficient and accurate categorization of WBC is a critical task for disease diagnosis by medical experts. This categorization helps in the correct identification of medical problems. In this research work, WBC classes are categorized with the help of a transform learning model in combination with our proposed virtual hexagonal trellis (VHT) structure feature extraction method. The VHT feature extractor is a kernel-based filter model designed over a square lattice. In the first step, Graft Net CNN model is used to extract features of augmented data set images. Later, the VHT base feature extractor extracts useful features. The CNN-extracted features are passed to ant colony optimization (ACO) module for optimal features acquisition. Extracted features from the VHT base filter and ACO are serially merged to create a single feature vector. The merged features are passed to the support vector machine (SVM) variants for optimal classification. Our strategy yields 99.9% accuracy, which outperforms other existing methods.

Navigated Transcranial Magnetic Stimulation (nTMS) based Preoperative Planning for Brain Tumor Treatment.

Transcranial Magnetic Stimulation (TMS) is a non-invasive technique for analyzing the central and peripheral nervous system. TMS could be a powerful therapeutic technique for neurological disorders. TMS has also shown potential in treating various neurophysiological complications, such as depression, anxiety, and obsessive-compulsive disorders, without pain and analgesics. Despite advancements in diagnosis and treatment, there has been an increase in the prevalence of brain cancer globally. For surgical planning, mapping brain tumors has proven challenging, particularly those localized in expressive regions. Preoperative brain tumor mapping may lower the possibility of postoperative morbidity in surrounding areas. A navigated TMS (nTMS) uses magnetic resonance imaging (MRI) to enable precise mapping during navigated brain stimulation. The resulting magnetic impulses can be precisely applied to the target spot in the cortical region by employing nTMS. This review focuses on nTMS for preoperative planning for brain cancer. This study reviews several studies on TMS and its subtypes in treating cancer and surgical planning. nTMS gives wider and improved dimensions of preoperative planning of the motor-eloquent areas in brain tumor patients. nTMS also predicts postoperative neurological deficits, which might be helpful in counseling patients. nTMS have the potential for finding possible abnormalities in the motor cortex areas.

Gastrointestinal tract disorders classification using ensemble of InceptionNet and proposed GITNet based deep feature with ant colony optimization.

Computer-aided classification of diseases of the gastrointestinal tract (GIT) has become a crucial area of research. Medical science and artificial intelligence have helped medical experts find GIT diseases through endoscopic procedures. Wired endoscopy is a controlled procedure that helps the medical expert in disease diagnosis. Manual screening of the endoscopic frames is a challenging and time taking task for medical experts that also increases the missed rate of the GIT disease. An early diagnosis of GIT disease can save human beings from fatal diseases. An automatic deep feature learning-based system is proposed for GIT disease classification. The adaptive gamma correction and weighting distribution (AGCWD) preprocessing procedure is the first stage of the proposed work that is used for enhancing the intensity of the frames. The deep features are extracted from the frames by deep learning models including InceptionNetV3 and GITNet. Ant Colony Optimization (ACO) procedure is employed for feature optimization. Optimized features are fused serially. The classification operation is performed by variants of support vector machine (SVM) classifiers, including the Cubic SVM (CSVM), Coarse Gaussian SVM (CGSVM), Quadratic SVM (QSVM), and Linear SVM (LSVM) classifiers. The intended model is assessed on two challenging datasets including KVASIR and NERTHUS that consist of eight and four classes respectively. The intended model outperforms as compared with existing methods by achieving an accuracy of 99.32% over the KVASIR dataset and 99.89% accuracy using the NERTHUS dataset.

Three-Dimensional Semantic Segmentation of Diabetic Retinopathy Lesions and Grading Using Transfer Learning.

Diabetic retinopathy (DR) is a drastic disease. DR embarks on vision impairment when it is left undetected. In this article, learning-based techniques are presented for the segmentation and classification of DR lesions. The pre-trained Xception model is utilized for deep feature extraction in the segmentation phase. The extracted features are fed to Deeplabv3 for semantic segmentation. For the training of the segmentation model, an experiment is performed for the selection of the optimal hyperparameters that provided effective segmentation results in the testing phase. The multi-classification model is developed for feature extraction using the fully connected (FC) MatMul layer of efficient-net-b0 and pool-10 of the squeeze-net. The extracted features from both models are fused serially, having the dimension of N × 2020, amidst the best N × 1032 features chosen by applying the marine predictor algorithm (MPA). The multi-classification of the DR lesions into grades 0, 1, 2, and 3 is performed using neural network and KNN classifiers. The proposed method performance is validated on open access datasets such as DIARETDB1, e-ophtha-EX, IDRiD, and Messidor. The obtained results are better compared to those of the latest published works.

An integrated framework for COVID-19 classification based on classical and quantum transfer learning from a chest radiograph.

COVID-19 is a quickly spreading over 10 million persons globally. The overall number of infected patients worldwide is estimated to be around 133,381,413 people. Infection rate is being increased on daily basis. It has also caused a devastating effect on the world economy and public health. Early stage detection of this disease is mandatory to reduce the mortality rate. Artificial intelligence performs a vital role for COVID-19 detection at an initial stage using chest radiographs. The proposed methods comprise of the two phases. Deep features (DFs) are derived from its last fully connected layers of pre-trained models like AlexNet and MobileNet in phase-I. Later these feature vectors are fused serially. Best features are selected through feature selection method of PCA and passed to the SVM and KNN for classification. In phase-II, quantum transfer learning model is utilized, in which a pre-trained ResNet-18 model is applied for DF collection and then these features are supplied as an input to the 4-qubit quantum circuit for model training with the tuned hyperparameters. The proposed technique is evaluated on two publicly available x-ray imaging datasets. The proposed methodology achieved an accuracy index of 99.0% with three classes including corona virus-positive images, normal images, and pneumonia radiographs. In comparison to other recently published work, the experimental findings show that the proposed approach outperforms it.

The association between two genetic polymorphisms in ITGB3 and increase risk of venous thromboembolism in cancer patients in Eastern Province of Saudi Arabia.

Venous thromboembolism (VTE) is one of the major complications in most cancer patients leading to poor prognosis and short survival. Several common clinical risk factors coexist in cancer patients are used as risk predictive biomarkers to help in the management and prevention of VTE. These include cancer site and stage, chemotherapy regimen and elevated biological markers. However, Genetic polymorphisms in genes controlling coagulation and fibrinolysis are significantly associated with VTE if detected, then they might be more sensitive individual predictive biomarkers for VTE risk assessment. This study was conducted to evaluate the association between ITGB3 rs3809865 and rs5918 with VTE risk as well as monitor the effect of VTE on overall survival of these cancer patients. In this retrospective case-control study, 195 cancer patients' formalin-fixed paraffin embedded tissue (FFPE) samples were collected (controls n = 157, case n = 38) using the stored data through Jan 2010 to Sep 2018 from King Fahad Specialist Hospital in Dammam. Samples were genotyped using TaqMan genotyping assay, then logistic regression analysis and Chi-square were used to predict the association between risk factors and VTE. Survival Comparison was tested by the log-rank test. Genetic polymorphisms in ITGB3 (rs3809865 and rs5918) found not to be associated with VTE increasing risk in cancer patients (p>0.05). While the advanced stage was potentially increasing the risk of VTE events (OR 5.1 CI 2.01-12.9p = 0.001). Patients with VTE showed a poor overall survival reflected by the median survival rate of only three years compared to seven years for cancer patients without VTE. This study highlighted the potential influence of VTE on prognosis and survival of cancer patients and raised the importance of exploring risk predictive biomarkers in our population. This will improve the risk prediction biomarkers leading to implementing safe and effective thrombosis prophylaxis strategies.

Metastatic Type II Papillary Renal Cell Carcinoma With Recurrent Complete Responses to Sunitinib: A Case Report With a Literature Review.

Papillary renal cell carcinoma (PRCC) is a less common subtype of kidney cancer and is typically more resistant to systemic treatments. This report describes a patient with metastatic type II PRCC who experienced two complete responses (CR) to the tyrosine kinase inhibitor (TKI) sunitinib. The patient remains on sunitinib with durable control of the disease. To the best of our knowledge, this is the first case of metastatic type II PRCC with CR to sunitinib.

Prevention of human papilloma virus infection with vaccines.

Human Papilloma virus (HPV) is present in all the cases of cervical cancer. It can also cause other diseases like genital warts, condylomata accuminata, cervical intraepithelial neoplasia and Anogenital cancers. Cervical cancer is the second most common cause of death from cancer. To improve the mortality from cervical cancers it is extremely important to prevent the HPV infection. In this review we have discussed the role of HPV vaccines in preventing the HPV infections and so the cervical cancer.

Sunitinib: An Unusual Cause of Pneumothorax in a Patient With Metastatic Chromophobe Renal Cell Carcinoma.

Spontaneous pneumothorax secondary to sunitinib, a vascular endothelial growth factor receptor (VEGFR) inhibitor, is an extremely rare side effect of this class of medications. In this report, we present the case of a patient with metastatic renal cell carcinoma (RCC) who developed bilateral pneumothoraces after starting on sunitinib. This case report recognizes pneumothorax as a life-threatening side effect of sunitinib.

Identifying a Novel DPYD Polymorphism Associated with Severe Toxicity to 5-FU Chemotherapy in a Saudi Patient.

Dihydropyrimidine dehydrogenase (DPD) is the major enzyme in the catabolism of 5-Fluorouracil (5-FU) and its prodrug capecitabine. We report a 65-year-old female with rectal adenocarcinoma who experienced severe toxicities secondary to standard dose 5-FU based chemotherapy. She was found to be heterozygous for rs371313778, c.2434G>A. This finding prompted restarting 5-FU at 50% dose reduction with further titration in subsequent cycles. We herein report the first case of rs371313778, c.2434G>A (p.Val812lle) DPYD polymorphism leading to severe 5-FU toxicities. The patient eventually completed a 6-month course of adjuvant treatment with modification of 5-FU dose.

Performance Status Assessment by Using ECOG (Eastern Cooperative Oncology Group) Score for Cancer Patients by Oncology Healthcare Professionals.

Medical literature does not have clear consensus on inter-rater reliability of PS assessment by different oncology health care professionals (HCPs) although it plays an important role in treatment decision and prognosis for oncology patients. Eastern Cooperative Oncology Group (ECOG) and Karnofsky performance status (KPS) scores are commonly used for this purpose by oncology HCPs around the world. This study was conducted to find variability or similarities in assessment of PS among the different oncology HCPs. A survey based on four hypothetical clinical scenarios was devised and sent to 50 oncology HCPs to assess the PS using ECOG PS tool. No significant variations in PS assessment by oncology HCPs was noted in our study sample.

Hepatitis B virus and hepatocarcinogenesis.

Hepatitis B viral (HBV) infection is the commonest cause of hepatocellular carcinoma. HBV DNA is the most important predictor of hepatocarcinogenesis in HB surface antigen positive patients. We reviewed the mechanism of hepatocarcinogenesis on molecular level with a special emphasis on the role of X-protein. Hepatitis B X-protein communicates with host targets and disturbs cellular functions including cell cycle regulation, apoptosis, signalling, transcriptional regulation, encoding of cytoskeleton, cell adhesion molecules, oncogenes and tumour suppressor genes.

Mechanisms of resistance to antiangiogenesis therapy.

Angiogenesis, the formation of new blood vessels from existing vasculature, plays an essential role in tumour growth, invasion and metastasis. Vascular endothelial growth factor (VEGF) is one of the key factors responsible for its regulation. High expression of VEGF has been observed in many cancers, and is associated with worse survival. When antiangiogenic agents are used alone they typically initially cause reduction in blood flow or vascular permeability, in many types of cancer. In some cases tumour regression occurs, mainly in renal cancer. In combination with chemotherapy, progression-free survival is often prolonged, but overall survival is not. Many tumours fail to respond initially - de novo resistance. Others develop resistance over time, with progression after a few months of treatment. The mechanisms of resistance are not well understood. The theoretical benefits of VEGF inhibitors are more likely to be realised by understanding these mechanisms and modifying therapy accordingly. This article reviews current knowledge on resistance mechanisms and the therapeutic implications.