Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images-two orders of magnitude larger than previous datasets-consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.
Globally breast cancer is the leading cause of cancer death among women. The breast consists of epithelium, stroma and a mucosal immune system that make up a complex microenvironment. Growing awareness of the role of microbes in the microenvironment recently has led to a series of findings important for human health. The microbiome has been implicated in cancer development and progression at a variety of body sites including stomach, colon, liver, lung, and skin. In this study, we assessed breast tissue microbial signatures in intraoperatively obtained samples using 16S rDNA hypervariable tag sequencing. Our results indicate a distinct breast tissue microbiome that is different from the microbiota of breast skin tissue, breast skin swabs, and buccal swabs. Furthermore, we identify distinct microbial communities in breast tissues from women with cancer as compared to women with benign breast disease. Malignancy correlated with enrichment in taxa of lower abundance including the genera Fusobacterium, Atopobium, Gluconacetobacter, Hydrogenophaga and Lactobacillus. This work confirms the existence of a distinct breast microbiome and differences between the breast tissue microbiome in benign and malignant disease. These data provide a foundation for future investigation on the role of the breast microbiome in breast carcinogenesis and breast cancer prevention.
A standardised, generic, validated approach to stratify the magnitude of clinical benefit that can be anticipated from anti-cancer therapies: The European Society for Medical Oncology Magnitude of Clinical Benefit Scale (ESMO-MCBS)
- Annals of oncology : official journal of the European Society for Medical Oncology / ESMO
- Published over 3 years ago
The value of any new therapeutic strategy or treatment is determined by the magnitude of its clinical benefit balanced against its cost. Evidence for clinical benefit from new treatment options is derived from clinical research, in particular phase III randomised trials, which generate unbiased data regarding the efficacy, benefit and safety of new therapeutic approaches. To date there is no standard tool for grading the magnitude of clinical benefit of cancer therapies, which may range from trivial (median progression-free survival advantage of only a few weeks) to substantial (improved long term survival). Indeed, in the absence of a standardised approach for grading the magnitude of clinical benefit, conclusions and recommendations derived from studies are often hotly disputed and very modest incremental advances have often been presented, discussed and promoted as major advances or “breakthroughs”. Recognising the importance of presenting clear and unbiased statements regarding the magnitude of the clinical benefit from new therapeutic approaches derived from high quality clinical trials the European Society for Medical Oncology (ESMO) has developed a validated and reproducible tool to assess the magnitude of clinical benefit for cancer medicines, the ESMO Magnitude of Clinical Benefit Scale (ESMO-MCBS). This tool uses a rational, structured and consistent approach to derive a relative ranking of the magnitude of clinically meaningful benefit that can be expected from a new anti-cancer treatment. The ESMO-MCBS is an important first step to the critical public policy issue of value in cancer care, helping to frame the appropriate use of limited public and personal resources to deliver cost effective and affordable cancer care. The ESMO-MCBS will be a dynamic tool and its criteria will be revised on a regular basis.
Integrated molecular pathology (IMP) approaches based on DNA mutational profiling accurately determine pancreatic cyst malignancy risk in patients lacking definitive diagnoses following endoscopic ultrasound imaging with fine-needle aspiration of fluid for cytology. In such cases, IMP ‘low-risk’ and ‘high-risk’ diagnoses reliably predict benign and malignant disease, respectively, and provide improved risk stratification for malignancy than a model of the 2012 International Consensus Guideline (ICG) recommendations. Our objective was to determine if initial adjunctive IMP testing influenced future real-world pancreatic cyst management decisions for intervention or surveillance relative to ICG recommendations, and if this benefitted patient outcomes.
To systematically evaluate the Bosniak classification, with malignancy rates of each Bosniak category, and to assess the effectiveness related to surgical treatment and oncological outcome, based on recurrence and/or metastasis.
Renal malignancies are among the most prevalent pediatric cancers. The most common is favorable histology Wilms tumor (FHWT), which has 5-year overall survival exceeding 90%. Other pediatric renal malignancies, including anaplastic Wilms tumor, clear cell sarcoma, malignant rhabdoid tumor, and renal cell carcinoma, have less favorable outcomes. Recent clinical trials have identified gain of chromosome 1q as a prognostic marker for FHWT. Upcoming studies will evaluate therapy adjustments based on this and other novel biomarkers. For high-risk renal tumors, new treatment regimens will incorporate biological therapies. A research blueprint, viewed from the perspective of the Children’s Oncology Group, is presented. Pediatr Blood Cancer © 2012 Wiley Periodicals, Inc.
Cancer is a malignant disease that has caused millions of human deaths. Its study has a long history of well over hundred years. There have been an enormous number of publications on cancer research. This integrated but unstructured biomedical text is of great value for cancer diagnostics, treatment, and prevention. The immense body and rapid growth of biomedical text on cancer has led to the appearance of a large number of text mining techniques aimed at extracting novel knowledge from scientific text. Biomedical text mining on cancer research is computationally automatic and high-throughput in nature. However, it is error-prone due to the complexity of natural language processing. In this review, we introduce the basic concepts underlying text mining and examine some frequently used algorithms, tools, and data sets, as well as assessing how much these algorithms have been utilized. We then discuss the current state-of-the-art text mining applications in cancer research and we also provide some resources for cancer text mining. With the development of systems biology, researchers tend to understand complex biomedical systems from a systems biology viewpoint. Thus, the full utilization of text mining to facilitate cancer systems biology research is fast becoming a major concern. To address this issue, we describe the general workflow of text mining in cancer systems biology and each phase of the workflow. We hope that this review can (i) provide a useful overview of the current work of this field; (ii) help researchers to choose text mining tools and datasets; and (iii) highlight how to apply text mining to assist cancer systems biology research.
Cancer of unknown primary site (CUP) comprises a relatively frequently occurring group of heterogeneous malignant tumors in the clinical routine, which currently has an abysmal prognosis for affected patients. Based on the improved diagnostic tools it is now possible to identify subgroups of patients with different clinical prognoses. New therapies adapted to these identified subgroups are becoming increasingly more relevant.
Malignant mesothelioma (MM) is an aggressive malignancy of the pleura and other serosal membranes originating from mesothelial cells that, despite decades of research, continues to have limited therapeutic options and is associated with a poor prognosis. Areas covered: MMs induce a strong inflammatory response that is also associated with neoangiogenesis and activation of proangiogenic factors. Given this, several anti-angiogenic agents have been trialled in a variety of malignancies including mesothelioma. Herein we summarise the role of angiogenesis in MM and the current available data targeting these pathways. Expert commentary: The addition of bevacizumab to cisplatin/pemetrexed chemotherapy is currently a therapeutic option with a proven 2.7 month overall survival benefit in fit patients less than 75. Other antiangiogenics such as nintedinib show early promise, although the Phase III trial results are eagerly awaited before this therapy enters treatment paradigms. Beyond this, it is likely that combinations of antiangiogenics with immunotherapies will be investigated in future studies.
Nanomedicine is an emerging field, which constitutes a new direction in the treatment of cancer. Magnetic nanoparticles (MNPs) can circumvent vascular tissue to concentrate at the site of the tumor. Under the influence of an external, alternating magnetic field, MNPs generate high temperatures within the tumor and ablate malignant cells while inflicting minimal damage to healthy host tissue. Due to their theranostic properties, they constitute a promising candidate for the treatment of cancer. A critical review of the type, size and therapeutic effect of different MNPs is presented, following an appraisal of the literature in the last 5 years. This is a multibillion dollar industry, with a few studies moving to clinical trials within the next 5 years.