Tumor-identification method for predicting recurrence of early-stage lung adenocarcinoma using digital pathology images by machine learning.

Lung cancer is one of the cancers with the highest morbidity and mortality in the world. Recurrence often occurs even after complete resection of early-stage lung cancer, and prediction of recurrence after resection is clinically important. However, the pathological characteristics of the recurrence of pathological stage IB lung adenocarcinoma (LAIB) have not yet been elucidated. Therefore, the problem is what type of histological image of lung adenocarcinoma recurs, and it is important to examine the histological image of recurrence. We attempted to predict recurrence of early lung adenocarcinoma after resection on the basis of digital pathological images of hematoxylin and eosin-stained specimens and machine learning applying a convolutional neural network. We constructed a model that extracts the features of two-color spaces and a switching model that automatically switches between our extraction model and one that extracts the features of one-color space for each image. We then developed a tumor-identification method for predicting the presence or absence of LAIB recurrence using these models. We conducted an experiment involving 55 patients with LAIB who underwent surgical resection to evaluate the proposed method. The proposed method determined LAIB recurrence with an accuracy of 84.8%. The use of digital pathology and machine learning can be used for highly accurate prediction of LAIB recurrence after surgical resection. The proposed method has the potential for objective postoperative follow-up observation.

Heat transfer analysis for peripheral blood flow measurement system.

Some disorders such as circulatory disease and metabolic abnormality cause many problems to peripheral blood flow condition. Therefore, frequent measurement of the blood flow condition is bound to contribute to precaution against those disorders and to control of conditions of the diseases. We propose a convenient means of blood flow volume measurement at peripheral part, such as fingertips. Principle of this measurement is based on heat transfer characteristics of peripheral part containing the blood flow. Transition response analysis of skin surface temperature has provided measurement model of the peripheral blood flow volume. We developed the blood flow measurement system based on that model and evaluated it by using artificial finger under various temperature conditions of ambience and internal fluid. The evaluation results indicated that proposed method could estimate the volume of the fluid regardless of temperature condition of them. Finally we applied our system to real finger testing and have obtained results correlated well with laser Doppler blood flow meter values.