Early Detection of Cervical Cancer by Fluorescence Lifetime Imaging Microscopy Combined with Unsupervised Machine Learning

Abstract

Cervical cancer has high morbidity and mortality rates, affecting hundreds of thousands of women worldwide and requiring more accurate screening for early intervention and follow-up treatment. Cytology is the current dominant clinical screening approach, and though it has been used for decades, it has unsatisfactory sensitivity and specificity. In this work, fluorescence lifetime imaging microscopy (FLIM) was used for the imaging of exfoliated cervical cells in which an endogenous coenzyme involved in metabolism, namely, reduced nicotinamide adenine dinucleotide (phosphate) [NAD(P)H], was detected to evaluate the metabolic status of cells. FLIM images from 71 participants were analyzed by the unsupervised machine learning method to build a prediction model for cervical cancer risk. The FLIM method combined with unsupervised machine learning (FLIM-ML) had a sensitivity and specificity of 90.9% and 100%, respectively, significantly higher than those of the cytology approach. One cancer recurrence case was predicted as high-risk several months earlier using this method as compared to using current clinical methods, implying that FLIM-ML may be very helpful for follow-up cancer care. This study illustrates the clinical applicability of FLIM-ML as a detection method for cervical cancer screening and a convenient tool for follow-up cancer care.

Supplementation with NAD+ and its precursors: A rescue of female reproductive diseases

“…Fluorescence lifetime imaging microscopy (FLIM) was used to analyze NAD(P)H in exfoliated cervical cells from healthy volunteers and patients with different degrees of cancerization. The results showed that cancer cells have a relatively short average fluorescence lifetime and less protein-bound NAD(P)H ratio and are more prone to glycolysis than OXPHOS compared with normal cells [ 106 ]. Chen et al [ 107 ] identified 293 NAD + metabolic-related genes and 21 prognostic NAD + metabolic-related genes in 39 CC patients: ADAMTS10, ANGPTL5, APCDD1L, CCDC85A, CGREF1, CHRDL2, CRP, DENND5B, EFS, FGF8, P4HA3, PCDH20, PCDHAC2, RASGRF2, S100P, SLC19A3, SLC6A14, TESC, TFPI, TNMD, ZNF229 , and for the first time described SLC19A3 as a prognostic signature through NAD + metabolism, providing clinical prognostic biomarkers and therapeutic targets for CC.…”

Optical imaging for early detection of cervical cancer: state of the art and perspectives

“…36 Ji et al combined FLIM with ML algorithms for imaging of exfoliated cervical cell samples that helps in predicting risk of cervical cancer in the patients with a sensitivity and specificity of 90.9% and 100%, respectively. 37 Wang et al used phasor analysis for the image analysis obtained from FLIM that helps in distinguishing normal, low, and, high grade cervical lesions based on the relationship between metabolic changes and cancer development. 38 Qiu et al performed a strong near-infrared absorption based fluorescence imaging for the early diagnosis of cervical cancer.…”

“…Ji et al. combined FLIM with ML algorithms for imaging of exfoliated cervical cell samples that helps in predicting risk of cervical cancer in the patients with a sensitivity and specificity of 90.9% and 100%, respectively 37 . Wang et al.…”

Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment

“…Furthermore, cervical cancer [66], skin cancer [67,68], oral cancer [69,70], esophageal squamous cell carcinoma and adenocarcinoma of the esophagogastric junction [71] can also be detected and distinguished early using AI models. The above studies greatly demonstrate the potential of AI models in detecting early cancers.…”