Perovskite Probe-Based Machine Learning Imaging Model for Rapid Pathologic Diagnosis of Cancers

Jimei Chi; Yonggan Xue; Yinying Zhou; Teng Han; Bobin Ning; Lijun Cheng; Hongfei Xie; Huadong Wang; Wenchen Wang; Qingyu Meng; Kaijie Fan; Fangming Gong; Junzhen Fan; Nan Jiang; Zheng Liu; Ke Pan; Hongyu Sun; Jiajin Zhang; Qian Zheng; Jiandong Wang; Meng Su; Yanlin Song

doi:10.1021/acsnano.4c06351

journal article·2024-09-03

Perovskite Probe-Based Machine Learning Imaging Model for Rapid Pathologic Diagnosis of Cancers

Jimei Chi, Yonggan Xue, Yinying Zhou, Teng Han, Bobin Ning, Lijun Cheng, Hongfei Xie, Huadong Wang, Wenchen Wang, Qingyu Meng, Kaijie Fan, Fangming Gong, Junzhen Fan, Nan Jiang, Zheng Liu, Ke Pan, Hongyu Sun, Jiajin Zhang, Qian Zheng, Jiandong Wang, Meng Su, Yanlin Song

ACS Nano· Volume 18· Issue 35

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Source abstract

Abstract

Accurately distinguishing tumor cells from normal cells is a key issue in tumor diagnosis, evaluation, and treatment. Fluorescence-based immunohistochemistry as the standard method faces the inherent challenges of the heterogeneity of tumor cells and the lack of big data analysis of probing images. Here, we have demonstrated a machine learning-driven imaging method for rapid pathological diagnosis of five types of cancers (breast, colon, liver, lung, and stomach) using a perovskite nanocrystal probe. After conducting the bioanalysis of survivin expression in five different cancers, high-efficiency perovskite nanocrystal probes modified with the survivin antibody can recognize the cancer tissue section at the single cell level. The tumor to normal (T/N) ratio is 10.3-fold higher than that of a conventional fluorescent probe, which can successfully differentiate between tumors and adjacent normal tissues within 10 min. The features of the fluorescence intensity and pathological texture morphology have been extracted and analyzed from 1000 fluorescence images by machine learning. The final integrated decision model makes the area under the receiver operating characteristic curve (area under the curve) value of machine learning classification of breast, colon, liver, lung, and stomach above 90% while predicting the tumor organ of 92% of positive patients. This method demonstrates a high T/N ratio probe in the precise diagnosis of multiple cancers, which will be good for improving the accuracy of surgical resection and reducing cancer mortality.

Source: openalex_inverted_index · Quality: baseline

Research context

Nanomaterials Electronic materials

OpenAlex topics: Advanced biosensing and bioanalysis techniques · Nanoplatforms for cancer theranostics · Advanced Biosensing Techniques and Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Key Laboratory of Green Printing, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences (ICCAS), Beijing Engineering Research Center of Nanomaterials for Green Printing Technology, Beijing National Laboratory for Molecular Sciences (BNLMS), Beijing 100190, P. R. China

University of Chinese Academy of Sciences, Beijing 100049, P. R. China

Department of General Surgery, the First Medical Centre, Chinese PLA General Hospital, Beijing, 100853, China

School of Software, Northwestern Polytechnical University, Xi'an, 710129, China

Institute of Software, Chinese Academy of Sciences, Beijing, 100191, China

Department of Thoracic Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China

Department of Pathology, the Third Medical Center, Chinese PLA General Hospital, Beijing, 100089, China

Faculty of Hepatopancreatobiliary Surgery, the First Medical Centre, Chinese PLA General Hospital, Beijing, 100853, China

Senior Department of Orthopedics, the Fourth Medical Center of PLA General Hospital, Beijing, 100048, China

Institute of Hepato-Pancreato-Biliary Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China

Department of Gastroenterology, the First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China

Funding

Beijing National Laboratory for Molecular Sciences · National Key Research and Development Program of China · Youth Innovation Promotion Association of the Chinese Academy of Sciences · National Natural Science Foundation of China

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