Abstract
High throughput and sufficient diagnostic accuracy of microscopical screening of cytological samples for the presence of cancer cells necessitates the use of highly qualified professionals. Using supervised machine learning, a programme was created that can categorise Feulgen-stained nuclei into eight diagnostically different types using commercially available, automated microscope-based screeners (MotiCyte and EasyScan). The nuclear DNA content was internally calibrated, using normal cells. The nuclei of cells that seemed to be malignant were recognized morphometrically. A blinded study was performed using oral smears from 92 patients with Fanconi anemia, revealing oral leukoplakias or erythroplakias. In a previous study, we evaluated the diagnostic accuracy of 121 samples of serous effusions. In addition, we sought to identify those whose tumours would not progress within 4 years using a blinded study with 80 prostate cancer patients who were receiving active surveillance. 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%, determined by conventional subjective cytological assessment using the same slides. Automated screening effusions, accuracy was 84.3%, while conventional cytology accuracy was 95.9%. Within 4.1 years, none of the prostate cancer patients under active monitoring with DNA grade 1 demonstrated disease progression. In order to identify malignant cells in various human specimen types with diagnostic accuracy on par with subjective cytological evaluation, an automated microscope-based screener was created. This automated method could detect early prostate tumours that do not spread while receiving no treatment.
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