aetherAI—Asia’s leading medical image AI solution provider focused on digital pathology and medical imaging AI—has announced the publication of its machine learning approach for the accurate quantification of nuclear morphometrics and differential diagnosis of primary intestinal T-cell lymphomas in the peer-reviewed Cancers. This approach not only brings deeper insight into lymphoma phenotypes but also paves the way for future discoveries concerning their relationship with disease classification and outcomes. Entitled “Machine Learning Based on Morphological Features Enables Classification of Primary Intestinal T-Cell Lymphomas,” the article can be accessed online here.
Fig.1: Sample images used as the dataset.
(A) An example of the whole-slide image. A high-quality region was manually marked with a black circle by a senior hematopathologist.
(B) Left to right: Representative HPF images of neoplastic lymphocytes from MEITL, borderline, and ITCL-NOS cases. The neoplastic lymphocytes detected and segmented by the model are contoured in red.
The aim of the study was to investigate the feasibility of using machine learning techniques based on morphological features to classify two subtypes of primary intestinal T-cell lymphomas (PITLs): monomorphic epitheliotropic intestinal T-cell lymphoma (MEITL) and intestinal T-cell lymphoma, not otherwise specified (ITCL-NOS), defined according to WHO criteria. These features are considered a major challenge for pathological diagnosis.
The results indicate that the XGBoost model gave superior classification performance to the end-to-end CNN model and could elucidate explicit relationships between predictions and morphological features, while also achieving results comparable to classifications incorporating immunophenotype or to work by senior hematopathologists.
The deep neural network achieved an average precision of 0.881 in the cell segmentation work. In its classification of MEITL versus ITCL-NOS, the XGBoost model achieved an area under the receiver operating characteristic curve (AUC) of 0.966.
Fig. 2: Comparison of contour segmentation results with the QuPath cell detection module. aetherAI’s lymphocyte detection model (third row) showed fewer FC and FP but more false negatives (FN) at a score threshold of 0.2.
“Our research demonstrates an accurate, human-interpretable approach to using machine learning algorithms to reduce the high dimensionality of image features and classify T cell lymphomas that present challenges in morphologic diagnosis,” said Joe Yeh, M.D., aetherAI CEO. The quantitative nuclear morphometric features may lead to further discoveries concerning the relationship between cellular phenotype and disease status. “Our model may have great potential to improve diagnostic consistency, efficiency, and accuracy for other types of cancers,” Yeh added.
The study was conducted in collaboration with Dr. Shih-Sung Chuang of the Department of Pathology at Chi-Mei Medical Center in Taiwan and with four other hospital departments of pathology. A total of 40 histopathological whole-slide images (WSIs) from 40 surgically resected PITL cases were used as the dataset for model training and testing.
數位病理及醫療影像AI領導廠商 - 雲象科技所研發的醫療影像AI應用，成功協助醫師對淋巴瘤可有更細緻且精確的診斷方法，這項研究成果已獲刊登於國際知名醫療期刊《Cancers》，肯定技術應用的突破。
T細胞淋巴瘤是少見的疾病，最重要的第一步是正確的病理診斷，精確診斷則仰賴病理科醫師對於腫瘤組織切片的形態分析。T細胞淋巴瘤有兩類在形態上難以區分的疾病：1、單形性上皮腸T細胞淋巴瘤 (MEITL，monomorphic epitheliotropic intestinal T-cell lymphoma)；2、腸道T細胞淋巴瘤 (ITCL-NOS，intestinal T-cell lymphoma, not otherwise specified)。病理診斷是透過細胞核大小變異度、形態分布、以及免疫表現型來區分這兩類疾病，對經驗尚在累積的一般病理科醫師挑戰甚鉅，而且形態介於兩個極端之間的案例也難以確切診斷。