Automated detection of cancer cells in effusion specimens by DNA karyometry

Abstract

Background The average sensitivity of conventional cytology for the identification of cancer cells in effusion specimens is only approximately 58%. DNA image cytometry (DNA‐ICM), which exploits the DNA content of morphologically suspicious nuclei measured on digital images, has a sensitivity of up to 91% for the detection of cancer cells. However, when performed manually, to our knowledge to date, an expert needs approximately 60 minutes for the analysis of a single slide. Methods In the current study, the authors present a novel method of supervised machine learning for the automated identification of morphologically suspicious mesothelial and epithelial nuclei in Feulgen‐stained effusion specimens. The authors compared this with manual DNA‐ICM and a gold standard cytological diagnosis for 121 cases. Furthermore, the authors retrospectively analyzed whether the amount of morphometrically abnormal mesothelial or epithelial nuclei detected by the digital classifier could be used as an additional diagnostic marker. Results The presented semiautomated DNA karyometric solution identified more diagnostically relevant abnormal nuclei compared with manual DNA‐ICM, which led to a higher sensitivity (76.4% vs 68.5%) at a specificity of 100%. The ratio between digitally abnormal and all mesothelial nuclei was found to identify cancer cell–positive slides at 100% sensitivity and 70% specificity. The time effort for an expert therefore is reduced to the verification of a few nuclei with exceeding DNA content, which to our knowledge can be accomplished within 5 minutes. Conclusions The authors have created and validated a computer‐assisted bimodal karyometric approach for which both nuclear morphology and DNA are quantified from a Feulgen‐stained slide. DNA karyometry thus increases the diagnostic accuracy and reduces the workload of an expert when compared with manual DNA‐ICM.

A Rapid Cytological Screening as pre-Endoscopy Screening for Early Esophageal Squamous Cell Lesions: A Prospective Pilot Study from a Chinese Academic Center

“…AI-assisted diagnosis provides a novel solution for boring pathological diagnosis, and also demonstrates a high efficiency. 18 , 27 In this study, a final cytological diagnosis for batch specimen can be completed within two working days by using a semi-automated detection assisted analysis. This present semi-automated diagnosis was superior to manual diagnosis by presenting a higher diagnostic efficiency, reproducibility and inter-observer agreement.…”

“…Application of artificial intelligence (AI) provides a novel method for solution of this dilemma of analyzing large amounts of bio-images. 17 , 18 By using a deep-learning approach, a computer-assisted screening system that has robust and fast cell recognition can be constructed. 18 Thus, great progress has been achieved in improving the diagnostic accuracy and effectiveness of preliminary assessments of a large number of cells.…”

“… 17 , 18 By using a deep-learning approach, a computer-assisted screening system that has robust and fast cell recognition can be constructed. 18 Thus, great progress has been achieved in improving the diagnostic accuracy and effectiveness of preliminary assessments of a large number of cells. 17 , 18 Although a computer-assisted analysis may promote the efficiency of cytological diagnosis, no such method for screening of early esophageal squamous lesions is available.…”

Diagnostic and Prognostic DNA-Karyometry for Cancer Diagnostics

“…were detected [29,30]. Doubtful, needs further evaluation, is assumed in the absence of DNA-stemline-or -singlecell-aneuploidy but > 1% morphologically abnormal/atypical nuclei [30].…”

DNA image cytometric analysis of bronchial washings as an adjunct for the detection of lung cancer in a clinical setting

“… 7 By cytometric evaluation of nuclear DNA content after stoichiometric staining of DNA, a DNA stem line in an abnormal histogram position is often termed a “DNA aneuploidy,” which has been thought to be a reliable marker of cancer. 8 , 9 Computer‐assisted DNA image cytometry (DNA‐ICM) is a sensitive method for the quantitative analysis of the nuclear structure and DNA content of individual cells. Several studies have demonstrated the advantage of DNA‐ICM in diagnosing various malignant cancer types with objectivity, convenience, and a high positive rate.…”