DNA Karyometry for Automated Detection of Cancer Cells.

Abstract

Simple SummaryCancers have to be microscopically established before they can be treated adequately. This can be performed on cell and/or tissue samples. Smears or sedimentations of cells on glass slides are used to microscopically screen large number of specimens for the presence of cancer cells. Such screenings require highly specialized personnel, which is not available in most countries. We present a microscope-based scanner which is able to identify cancer cells in smears from the oral cavity or in body cavity fluids. In addition, using the same system, the degree of malignancy of prostate cancer can be determined. A combination of image features of stained nuclei and their DNA-content is used to raise a suspicion of malignancy. Nuclear diagnostic classifiers were trained by an expert using supervised machine learning. All device-proposed diagnoses can be verified by a specialist. The overall percentage of correct device-derived diagnoses on oral smears was 91.3% as compared to 75.0% for conventional, subjective investigation.Background: Microscopical screening of cytological samples for the presence of cancer cells at high throughput with sufficient diagnostic accuracy requires highly specialized personnel which is not available in most countries. Methods: Using commercially available automated microscope-based screeners (MotiCyte and EasyScan), software was developed which is able to classify Feulgen-stained nuclei into eight diagnostically relevant types, using supervised machine learning. the nuclei belonging to normal cells were used for internal calibration of the nuclear DNA content while nuclei belonging to those suspicious of being malignant were specifically identified. The percentage of morphologically abnormal nuclei was used to identify samples suspected of malignancy, and the proof of DNA-aneuploidy was used to definitely determine the state malignancy. A blinded study was performed using oral smears from 92 patients with Fanconi anemia, revealing oral leukoplakias or erythroplakias. In an earlier study, we compared diagnostic accuracies on 121 serous effusion specimens. In addition, using a blinded study employing 80 patients with prostate cancer who were under active surveillance, we aimed to identify those whose cancers would not advance within 4 years. Results: Applying a threshold of the presence of >4% of morphologically abnormal nuclei from oral squamous cells and DNA single-cell or stemline aneuploidy to identify samples suspected of malignancy, an overall diagnostic accuracy of 91.3% was found as compared with 75.0% accuracy determined by conventional subjective cytological assessment using the same slides. Accuracy of automated screening effusions was 84.3% as compared to 95.9% of conventional cytology. No prostate cancer patients under active surveillance, revealing DNA-grade 1, showed progress of their disease within 4.1 years. Conclusions: An automated microscope-based screener was developed which is able to identify malignant cells in different types of human specimens with a diagnostic accuracy comparable with subjective cytological assessment. Early prostate cancers which do not progress despite applying any therapy could be identified using this automated approach.