Weakly-supervised learning for lung carcinoma classification using deep learning

Abstract Lung cancer is one of the major causes of cancer-related deaths in many countries around the world, and its histopathological diagnosis is crucial for deciding on optimum treatment strategies. Recently, Artificial Intelligence (AI) deep learning models have been widely shown to be useful in various medical fields, particularly image and pathological diagnoses; however, AI models for the pathological diagnosis of pulmonary lesions that have been validated on large-scale test sets are yet to be seen. We trained a Convolution Neural Network (CNN) based on the EfficientNet-B3 architecture, using transfer learning and weakly-supervised learning, to predict carcinoma in Whole Slide Images (WSIs) using a training dataset of 3,554 WSIs. We obtained highly promising results for differentiating between lung carcinoma and non-neoplastic with high Receiver Operator Curve (ROC) area under the curves (AUCs) on four independent test sets (ROC AUCs of 0.975, 0.974, 0.988, and 0.981, respectively). Development and validation of algorithms such as ours are important initial steps in the development of software suites that could be adopted in routine pathological practices and potentially help reduce the burden on pathologists

Assessment of pleural air leakage using digital chest drainage system after surgical pulmonary resection: Comparison of visible alveolar air leakage with the digital value measured by a digital chest drainage system

<div><p>Background</p><p>The sensitivity of postoperative pleural air leakage (PAL) after pulmonary resection is evaluated by a simple subjective grading method in clinical practice. A new electronic digital chest drainage evaluation system (DCS) recently became clinically available. This study was designed to evaluate the clinical application of the DCS in monitoring the airflow volume and managing postoperative PAL.</p><p>Methods</p><p>We prospectively enrolled 25 patients who underwent pulmonary resection. Postoperative PAL was evaluated using both conventional PAL grading based on the physician’s visual judgment (analog chest drainage evaluation system [ACS]: Level 0 = no leakage to 4 = continuous leakage) and the DCS. The DCS digital measurement was recorded as the flow volume (ml/min), which was taken once daily from postoperative day 1 to the day of chest drainage tube removal.</p><p>Results</p><p>In total, 45 measurements were performed on 25 patients during the evaluation period. Postoperative PAL was observed in five patients (20.0%) and judged as ACS Level >1. The mean DCS values corresponding to ACS Levels 0, 1, 2, and 3 were 2.42 (0.0–11.3), 48.6 (35.4–67.9), 95.6 (79.7–111.5), and 405.3 (150.3–715.6), respectively. The Spearman correlation test showed a significant positive correlation between the ACS PAL level and DCS flow volume (<i>R</i> = 0.8477, <i>p</i> < 0.001).</p><p>Conclusions</p><p>A relationship between the visual PAL level by the ACS and the digital value by the DCS was identified in this study. The numeric volume obtained by the DCS has been successful in information-sharing with all staff. The digital PAL value evaluated by the DCS is appropriate, and the use of the DCS is promising in the treatment of postoperative PAL after pulmonary resection.</p></div

Connection and measurement of the ACS and DCS.

<p>(A) Normal state. (B) Measurement state.</p

Comparison between PAL level and actual value using DCS.

<p>Comparison between PAL level and actual value using DCS.</p

Clinical profiles of 25 patients enrolled in this study.

<p>Clinical profiles of 25 patients enrolled in this study.</p

Timing of chest drainage tube removal in 24 patients.

<p>Timing of chest drainage tube removal in 24 patients.</p