Machine learning methods for histopathological image analysis

Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.Comment: 23 pages, 4 figure

From detection of individual metastases to classification of lymph node status at the patient level: the CAMELYON17 challenge

Automated detection of cancer metastases in lymph nodes has the potential to improve the assessment of prognosis for patients. To enable fair comparison between the algorithms for this purpose, we set up the CAMELYON17 challenge in conjunction with the IEEE International Symposium on Biomedical Imaging 2017 Conference in Melbourne. Over 300 participants registered on the challenge website, of which 23 teams submitted a total of 37 algorithms before the initial deadline. Participants were provided with 899 whole-slide images (WSIs) for developing their algorithms. The developed algorithms were evaluated based on the test set encompassing 100 patients and 500 WSIs. The evaluation metric used was a quadratic weighted Cohen's kappa. We discuss the algorithmic details of the 10 best pre-conference and two post-conference submissions. All these participants used convolutional neural networks in combination with pre- and postprocessing steps. Algorithms differed mostly in neural network architecture, training strategy, and pre- and postprocessing methodology. Overall, the kappa metric ranged from 0.89 to -0.13 across all submissions. The best results were obtained with pre-trained architectures such as ResNet. Confusion matrix analysis revealed that all participants struggled with reliably identifying isolated tumor cells, the smallest type of metastasis, with detection rates below 40%. Qualitative inspection of the results of the top participants showed categories of false positives, such as nerves or contamination, which could be targeted for further optimization. Last, we show that simple combinations of the top algorithms result in higher kappa metric values than any algorithm individually, with 0.93 for the best combination

Association of circulating histone H3 and high mobility group box 1 levels with postoperative prognostic indicators in intensive care unit patients: a single-center observational study

Objectives: Damage associated molecular patterns (DAMPs) levels are associated with sepsis severity and prognosis. Histone and high mobility group box 1 (HMGB1) levels are also potential indicators of prognosis. We investigated the relationship between serum histone H3 and HMGB1 levels and the illness severity score and prognosis in postoperative patients. Methods: Postoperative serum histone H3 and HMGB1 levels in 39 intensive care unit (ICU) patients treated at our institution were measured. The correlation between peak histone H3 and HMGB1 levels in each patient and clinical data (age, sex, surgical time, length of ICU stay, and survival after ICU discharge), which also included the patients’ illness severity score, was examined. Results: Histone H3 but not HMGB1 levels were positively correlated with surgical time, the Sequential Organ Failure Assessment score, the Japanese Association for Acute Medicine acute phase disseminated intravascular coagulation diagnosis score, and the length of ICU stay. Both histone H3 and HMGB1 levels were negatively correlated with age. However, survival post-ICU discharge was not correlated with histone H3 or HMGB1 levels. Conclusions: Histone H3 levels are correlated with severity scores and the length of ICU stay. Serum histone H3 and HMGB1 levels are elevated postoperatively. These DAMPs, however, are not prognostic indicators in postoperative ICU patients

Suppressive effects of the neutrophil elastase inhibitor sivelestat sodium hydrate on interleukin-1β production in lipopolysaccharide-stimulated porcine whole blood

OBJECTIVE: Sivelestat sodium hydrate (Siv) is expected to be an effective therapy for acute respiratory distress syndrome, although its mechanism of action is not understood. In this study, we investigated which myeloid cells-derived cytokines were suppressed by Siv. METHODS: Continuous hemofiltration was performed by circulating fresh porcine blood through a semi-closed circuit. To ensure that leukocytes survived for 360 min, 5% glucose, heparin, and air were continuously injected. The control group received continuous administration of lipopolysaccharide (LPS) only, whereas the Siv group received LPS and Siv. Complete blood count, levels of various cytokines, and other variables were compared between the groups. RESULTS: Interleukin (IL)-1β level was significantly suppressed in the Siv group compared with that in the control group (p<0.05). CONCLUSIONS: The results suggested that Siv suppressed the production of IL-1β and possibly other cytokines by myeloid cells. Whether this suppression of cytokine production is caused directly by Siv or mediated via suppression of granulocyte elastase should be evaluated in the future