Automated Report Generation for Lung Cytological Images Using a CNN Vision Classifier and Multiple-Transformer Text Decoders: Preliminary Study

Cytology plays a crucial role in lung cancer diagnosis. Pulmonary cytology involves cell morphological characterization in the specimen and reporting the corresponding findings, which are extremely burdensome tasks. In this study, we propose a report-generation technique for lung cytology images. In total, 71 benign and 135 malignant pulmonary cytology specimens were collected. Patch images were extracted from the captured specimen images, and the findings were assigned to each image as a dataset for report generation. The proposed method consists of a vision model and a text decoder. In the former, a convolutional neural network (CNN) is used to classify a given image as benign or malignant, and the features related to the image are extracted from the intermediate layer. Independent text decoders for benign and malignant cells are prepared for text generation, and the text decoder switches according to the CNN classification results. The text decoder is configured using a Transformer that uses the features obtained from the CNN for report generation. Based on the evaluation results, the sensitivity and specificity were 100% and 96.4%, respectively, for automated benign and malignant case classification, and the saliency map indicated characteristic benign and malignant areas. The grammar and style of the generated texts were confirmed as correct and in better agreement with gold standard compared to existing LLM-based image-captioning methods and single-text-decoder ablation model. These results indicate that the proposed method is useful for pulmonary cytology classification and reporting.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Transmission of bacterial infections to healthcare workers during intubation and respiratory care of patients with severe pneumonia

Exposure of healthcare workers to patients with rapidly fatal infections invariably raises concerns regarding the risk of occupational acquisition. We describe acquisition of Streptococcus pyogenes by 2 nurses from a patient with fatal pneumonia and review previously reported cases of transmission of bacterial pathogens from patients with pneumonia to healthcare workers

Automated classification of pulmonary nodules through a retrospective analysis of conventional CT and two-phase PET images in patients undergoing biopsy

Objective(s): Positron emission tomography/computed tomography (PET/CT) examination is commonly used for the evaluation of pulmonary nodules since it provides both anatomical and functional information. However, given the dependence of this evaluation on physician’s subjective judgment, the results could be variable. The purpose of this study was to develop an automated scheme for the classification of pulmonary nodules using early and delayed phase PET/ CT and conventional CT images.Methods: We analysed 36 early and delayed phase PET/CT images in patients who underwent both PET/CT scan and lung biopsy, following bronchoscopy. In addition, conventional CT images at maximal inspiration were analysed. The images consisted of 18 malignant and 18 benign nodules. For the classification scheme, 25 types of shape and functional features were first calculated from the images. The random forest algorithm, which is a machine learning technique, was used for classification.Results: The evaluation of the characteristic features and classification accuracy was accomplished using collected images. There was a significant difference between the characteristic features of benign and malignant nodules with regard to standardised uptake value and texture. In terms of classification performance, 94.4% of the malignant nodules were identified correctly assuming that 72.2% of the benign nodules were diagnosed accurately. The accuracy rate of benign nodule detection by means of CT plus two-phase PET images was 44.4% and 11.1% higher than those obtained by CT images alone and CT plus early phase PET images, respectively.Conclusion: Based on the findings, the proposed method may be useful to improve the accuracy of malignancy analysis

The Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2016 (J-SSCG 2016)

Background and purposeThe Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2016 (J-SSCG 2016), a Japanese-specific set of clinical practice guidelines for sepsis and septic shock created jointly by the Japanese Society of Intensive Care Medicine and the Japanese Association for Acute Medicine, was first released in February 2017 and published in the Journal of JSICM, [2017; Volume 24 (supplement 2)] https://doi.org/10.3918/jsicm.24S0001 and Journal of Japanese Association for Acute Medicine [2017; Volume 28, (supplement 1)] http://onlinelibrary.wiley.com/doi/10.1002/jja2.2017.28.issue-S1/issuetoc.This abridged English edition of the J-SSCG 2016 was produced with permission from the Japanese Association of Acute Medicine and the Japanese Society for Intensive Care Medicine.MethodsMembers of the Japanese Society of Intensive Care Medicine and the Japanese Association for Acute Medicine were selected and organized into 19 committee members and 52 working group members. The guidelines were prepared in accordance with the Medical Information Network Distribution Service (Minds) creation procedures. The Academic Guidelines Promotion Team was organized to oversee and provide academic support to the respective activities allocated to each Guideline Creation Team. To improve quality assurance and workflow transparency, a mutual peer review system was established, and discussions within each team were open to the public. Public comments were collected once after the initial formulation of a clinical question (CQ) and twice during the review of the final draft. Recommendations were determined to have been adopted after obtaining support from a two-thirds (> 66.6%) majority vote of each of the 19 committee members.ResultsA total of 87 CQs were selected among 19 clinical areas, including pediatric topics and several other important areas not covered in the first edition of the Japanese guidelines (J-SSCG 2012). The approval rate obtained through committee voting, in addition to ratings of the strengths of the recommendation, and its supporting evidence were also added to each recommendation statement. We conducted meta-analyses for 29 CQs. Thirty-seven CQs contained recommendations in the form of an expert consensus due to insufficient evidence. No recommendations were provided for five CQs.ConclusionsBased on the evidence gathered, we were able to formulate Japanese-specific clinical practice guidelines that are tailored to the Japanese context in a highly transparent manner. These guidelines can easily be used not only by specialists, but also by non-specialists, general clinicians, nurses, pharmacists, clinical engineers, and other healthcare professionals

Different Response to Nivolumab in a Patient with Synchronous Double Primary Carcinomas of Hypopharyngeal Cancer and Non-Small-Cell Lung Cancer

Nivolumab is a humanized IgG4 and programmed death 1 (PD-1) monoclonal antibody that has demonstrated antitumor efficacy in clinical trials of various malignant tumors including non-small-cell lung cancer and head and neck squamous cell carcinoma (SCC). However, patients with multiple primary malignancies were excluded in clinical trials. Thus, the efficacy of nivolumab in such patients has not been revealed yet. The programmed death ligand 1 (PD-L1) expression level is currently the main predictive biomarker of PD-1 inhibitors in various types of solid tumors and hematological malignancies. Here we describe a patient with synchronous double primary carcinomas of hypopharyngeal SCC and lung adenocarcinoma who exhibited different responses to nivolumab. After nivolumab treatment, hypopharyngeal SCC with moderate PD-L1 positivity by immunohistochemical staining showed a remarkable response; conversely, nivolumab was not effective against lung adenocarcinoma, which was negative for PD-L1. This suggests that tumors with different PD-L1 expressions may exhibit different responses to PD-1 inhibitors when multiple primary malignancies are present within one patient