Explore the vast range of titles available.

The American Cancer Society Head and Neck Cancer Survivorship Care Guideline was developed to assist primary care clinicians and other health practitioners with the care of head and neck cancer survivors, including monitoring for recurrence, screening for second primary cancers, assessment and management of long-term and late effects, health promotion, and care coordination. A systematic review of the literature was conducted using PubMed through April 2015, and a multidisciplinary expert workgroup with expertise in primary care, dentistry, surgical oncology, medical oncology, radiation oncology, clinical psychology, speech-language pathology, physical medicine and rehabilitation, the patient perspective, and nursing was assembled. While the guideline is based on a systematic review of the current literature, most evidence is not sufficient to warrant a strong recommendation. Therefore, recommendations should be viewed as consensus-based management strategies for assisting patients with physical and psychosocial effects of head and neck cancer and its treatment.

Skin conditions affect 1.9 billion people. Because of a shortage of dermatologists, most cases are seen instead by general practitioners with lower diagnostic accuracy. We present a deep learning system (DLS) to provide a differential diagnosis of skin conditions using 16,114 de-identified cases (photographs and clinical data) from a teledermatology practice serving 17 sites. The DLS distinguishes between 26 common skin conditions, representing 80% of cases seen in primary care, while also providing a secondary prediction covering 419 skin conditions. On 963 validation cases, where a rotating panel of three board-certified dermatologists defined the reference standard, the DLS was non-inferior to six other dermatologists and superior to six primary care physicians (PCPs) and six nurse practitioners (NPs) (top-1 accuracy: 0.66 DLS, 0.63 dermatologists, 0.44 PCPs and 0.40 NPs). These results highlight the potential of the DLS to assist general practitioners in diagnosing skin conditions. A deep learning system able to identify the most common skin conditions may help clinicians in making more accurate diagnoses in routine clinical practice

AbstractInfectious animal diseases caused by pathogenic microorganisms such as bacteria and viruses threaten the health and well-being of wildlife, livestock, and human populations, limit productivity and increase significantly economic losses to each sector. The pathogen detection is an important step for the diagnostics, successful treatment of animal infection diseases and control management in farms and field conditions. Current techniques employed to diagnose pathogens in livestock and poultry include classical plate-based methods and conventional biochemical methods as enzyme-linked immunosorbent assays (ELISA). These methods are time-consuming and frequently incapable to distinguish between low and highly pathogenic strains. Molecular techniques such as polymerase chain reaction (PCR) and real time PCR (RT-PCR) have also been proposed to be used to diagnose and identify relevant infectious disease in animals. However these DNA-based methodologies need isolated genetic materials and sophisticated instruments, being not suitable for in field analysis. Consequently, there is strong interest for developing new swift point-of-care biosensing systems for early detection of animal diseases with high sensitivity and specificity. In this review, we provide an overview of the innovative biosensing systems that can be applied for livestock pathogen detection. Different sensing strategies based on DNA receptors, glycan, aptamers and antibodies are presented. Besides devices still at development level some are validated according to standards of the World Organization for Animal Health and are commercially available. Especially, paper-based platforms proposed as an affordable, rapid and easy to perform sensing systems for implementation in field condition are included in this review.

A pathological evaluation is one of the most important methods for the diagnosis of malignant lymphoma. A standardized diagnosis is occasionally difficult to achieve even by experienced hematopathologists. Therefore, established procedures including a computer-aided diagnosis are desired. This study aims to classify histopathological images of malignant lymphomas through deep learning, which is a computer algorithm and type of artificial intelligence (AI) technology. We prepared hematoxylin and eosin (H&E) slides of a lesion area from 388 sections, namely, 259 with diffuse large B-cell lymphoma, 89 with follicular lymphoma, and 40 with reactive lymphoid hyperplasia, and created whole slide images (WSIs) using a whole slide system. WSI was annotated in the lesion area by experienced hematopathologists. Image patches were cropped from the WSI to train and evaluate the classifiers. Image patches at magnifications of ×5, ×20, and ×40 were randomly divided into a test set and a training and evaluation set. The classifier was assessed using the test set through a cross-validation after training. The classifier achieved the highest levels of accuracy of 94.0%, 93.0%, and 92.0% for image patches with magnifications of ×5, ×20, and ×40, respectively, in comparison to diffuse large B-cell lymphoma, follicular lymphoma, and reactive lymphoid hyperplasia. Comparing the diagnostic accuracies between the proposed classifier and seven pathologists, including experienced hematopathologists, using the test set made up of image patches with magnifications of ×5, ×20, and ×40, the best accuracy demonstrated by the classifier was 97.0%, whereas the average accuracy achieved by the pathologists using WSIs was 76.0%, with the highest accuracy reaching 83.3%. In conclusion, the neural classifier can outperform pathologists in a morphological evaluation. These results suggest that the AI system can potentially support the diagnosis of malignant lymphoma. This study aims to classify histopathological images of malignant lymphoma through deep learning. The classifier achieved the high levels of accuracy in comparison to diffuse large B-cell lymphoma, follicular lymphoma, and reactive lymphoid hyperplasia, which were higher than those of pathologists. Artificial intelligence can potentially support diagnosis of malignant lymphoma.

Background: Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a well-established first-line invasive modality for mediastinal lymph node staging in lung cancer patients and in the diagnostic workup of patients with mediastinal adenopathy. With the current 21- and 22-gauge (G) EBUS-TBNA needles, the procedure can be limited by the degree of flexibility in the needle and the size of the lumen in tissue acquisition. Objective: We report our initial experience with a first-generation flexible 19-G EBUS-TBNA (Flex 19G; Olympus Respiratory America, Redmond, WA, USA) needle with regards to efficacy and safety. Methods: The Flex 19G EBUS-TBNA needle was used in 47 selected patients with enlarged hilar and/or mediastinal lymphadenopathy at 3 centers. The standard Olympus EBUS scope with a 2.2-mm working channel was used in all cases. Results: The diagnostic yield of the Flex 19G needle according to clinical cytopathology reports was 89% (42/47). The diagnosis and their respective diagnostic yield with the Flex 19G EBUS-TBNA needle were malignancy 24/27 (89%), sarcoidosis 13/14 (93%), and reactive lymph node hyperplasia 5/6 (83%). The mean short axis of the sampled lymph nodes was 19 ± 9 mm. No complications occurred except for 1 instance of moderate bleeding, which did not require intervention beyond suctioning and subsequently resolved. All 13 patients diagnosed with adenocarcinoma by the 19-G needle had sufficient tissue for genetic testing. Conclusion: EBUS-TBNA using the first-generation Flex 19G needle is feasible and safe with promising diagnostic yield while providing a greater degree of flexion with the Olympus EBUS scope. Additional clinical evaluations are warranted.

Background/Aim The current computer-aided diagnosis (CADx) systems are not trained to recognize sessile-serrated lesions (SSL) and currently classify SSL incorrectly either as hyperplastic or adenomatous. We aimed to assess the effect of current untrained CADx systems on diagnostic accuracy of SSL recognition among a diverse group of endoscopists. Methods 17 endoscopists reviewed the same video and image series of 115 polyps [33% SL, 33% Hyperplastic Polyps (HP), and 34% tubular adenoma (TA)]. Presentation of polyps (SSL, TA, HP) was randomized with 50% of the polyps being displayed with a CADx diagnosis, and other 50% without. Results CADx identified 52.6% of SSL as HP and 47.4% as TA. CADx identified 68.4% of HP correctly and 82.1% of TA correctly. The accuracy of the endoscopists for SSL did not differ significantly when the CADx diagnosis was displayed compared to no CADx being displayed (64.1% vs 64.9%, p  > 0.05). The diagnostic accuracy for HP and TA improved among endoscopists when a correct CADx diagnosis was displayed to the endoscopists compared to when no CADx information was displayed but worsened when the displayed CADx diagnosis was incorrect. Conclusion Current CADx systems when displaying correct diagnostic outputs for HP or TA improve diagnostic accuracy of the endoscopists. CADx systems not trained for SSL recognition do not significantly affect SSL recognition among endoscopists even if an incorrect diagnostic output (TA or HP) is displayed.

Clinicians are required to assess abnormal liver chemistries on a daily basis. The most common liver chemistries ordered are serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase and bilirubin. These tests should be termed liver chemistries or liver tests. Hepatocellular injury is defined as disproportionate elevation of AST and ALT levels compared with alkaline phosphatase levels. Cholestatic injury is defined as disproportionate elevation of alkaline phosphatase level as compared with AST and ALT levels. The majority of bilirubin circulates as unconjugated bilirubin and an elevated conjugated bilirubin implies hepatocellular disease or cholestasis. Multiple studies have demonstrated that the presence of an elevated ALT has been associated with increased liver-related mortality. A true healthy normal ALT level ranges from 29 to 33 IU/l for males, 19 to 25 IU/l for females and levels above this should be assessed. The degree of elevation of ALT and or AST in the clinical setting helps guide the evaluation. The evaluation of hepatocellular injury includes testing for viral hepatitis A, B, and C, assessment for nonalcoholic fatty liver disease and alcoholic liver disease, screening for hereditary hemochromatosis, autoimmune hepatitis, Wilson's disease, and alpha-1 antitrypsin deficiency. In addition, a history of prescribed and over-the-counter medicines should be sought. For the evaluation of an alkaline phosphatase elevation determined to be of hepatic origin, testing for primary biliary cholangitis and primary sclerosing cholangitis should be undertaken. Total bilirubin elevation can occur in either cholestatic or hepatocellular diseases. Elevated total serum bilirubin levels should be fractionated to direct and indirect bilirubin fractions and an elevated serum conjugated bilirubin implies hepatocellular disease or biliary obstruction in most settings. A liver biopsy may be considered when serologic testing and imaging fails to elucidate a diagnosis, to stage a condition, or when multiple diagnoses are possible.

Focal liver lesions (FLL) have been a common reason for consultation faced by gastroenterologists and hepatologists. The increasing and widespread use of imaging studies has led to an increase in detection of incidental FLL. It is important to consider not only malignant liver lesions, but also benign solid and cystic liver lesions such as hemangioma, focal nodular hyperplasia, hepatocellular adenoma, and hepatic cysts, in the differential diagnosis. In this ACG practice guideline, the authors provide an evidence-based approach to the diagnosis and management of FLL.

Background Stage at diagnosis strongly predicts cancer survival and understanding related inequalities could guide interventions. Methods We analysed incident cases diagnosed with 10 solid tumours included in the UK government target of 75% of patients diagnosed in TNM stage I/II by 2028. We examined socio-demographic differences in diagnosis at stage III/IV vs. I/II. Multiple imputation was used for missing stage at diagnosis (9% of tumours). Results Of the 202,001 cases, 57% were diagnosed in stage I/II (an absolute 18% ‘gap’ from the 75% target). The likelihood of diagnosis at stage III/IV increased in older age, though variably by cancer site, being strongest for prostate and endometrial cancer. Increasing level of deprivation was associated with advanced stage at diagnosis for all sites except lung and renal cancer. There were, inconsistent in direction, sex inequalities for four cancers. Eliminating socio-demographic inequalities would translate to 61% of patients with the 10 studied cancers being diagnosed at stage I/II, reducing the gap from target to 14%. Conclusions Potential elimination of socio-demographic inequalities in stage at diagnosis would make a substantial, though partial, contribution to achieving stage shift targets. Earlier diagnosis strategies should additionally focus on the whole population and not only the high-risk socio-demographic groups.

Consultation for liver disease in pregnant women is a common and oftentimes vexing clinical consultation for the gastroenterologist. The challenge lies in the need to consider the safety of both the expectant mother and the unborn fetus in the clinical management decisions. This practice guideline provides an evidence-based approach to common diagnostic and treatment challenges of liver disease in pregnant women.