Role of DNA ploidy in diagnosis and prognosis of high-grade cervical intraepithelial neoplasia: A prospective cohort study.

OBJECTIVE: To compare the sensitivity and specificity of DNA ploidy with cytology, human papillomavirus (HPV) testing and colposcopy in diagnosis of high-grade cervical intraepithelial neoplasia (CIN) and to assess the role of aneuploidy in cervical lesions with the worst prognosis. A prospective observational cohort study was conducted on 254 women with altered colpocytology. METHODS: Colposcopy, biopsy, DNA-ICM and HPV examinations were applied to cervical cytological and histological samples. Participants were evaluated every 6 months and divided into two groups: 'Harm' and 'No-harm'. Logistic regression and multivariate COX model were used to identify independent risk factors for diagnosis and prognosis of high-grade CIN, and ROC curve to assess the sensitivity and specificity of methods. RESULTS: Variables 'age greater than or equal to 30 years', 'lesion size greater than 20%', 'aneuploidy' and 'HPV 16' were associated with diagnosis of high-grade CIN and 'aneuploidy' and 'women living with HIV', with a worse prognosis. Agreement for colposcopy was good, with a sensitivity of 79.3% and specificity of 94.4%; DNA-ICM and cytology were moderate, with sensitivity of 74.6% and 72.3% and specificity of 85.3% and 76.1%, respectively. High-risk HPV and HPV 16 tests were weak, with sensitivity of 75.0% and 43.75% and specificity of 50.0% and 88.64%, respectively. CONCLUSIONS: In relation to high-grade CIN diagnosis, DNA-ICM presented similar sensitivity and specificity to cytology and high-risk HPV test when associated with HPV 16. Regarding prognosis, this research certifies that aneuploidy is considered a predictor of more severe cervical injury.

What is the state of the art of computer vision-assisted cytology? A Systematic Literature Review.

Cytology is a low-cost and non-invasive diagnostic procedure employed to support the diagnosis of a broad range of pathologies. Cells are harvested from tissues by aspiration or scraping, and it is still predominantly performed manually by medical or laboratory professionals extensively trained for this purpose. It is a time-consuming and repetitive process where many diagnostic criteria are subjective and vulnerable to human interpretation. Computer Vision technologies, by automatically generating quantitative and objective descriptions of examinations' contents, can help minimize the chances of misdiagnoses and shorten the time required for analysis. To identify the state-of-art of computer vision techniques currently applied to cytology, we conducted a Systematic Literature Review, searching for approaches for the segmentation, detection, quantification, and classification of cells and organelles using computer vision on cytology slides. We analyzed papers published in the last 4 years. The initial search was executed in September 2020 and resulted in 431 articles. After applying the inclusion/exclusion criteria, 157 papers remained, which we analyzed to build a picture of the tendencies and problems present in this research area, highlighting the computer vision methods, staining techniques, evaluation metrics, and the availability of the used datasets and computer code. As a result, we identified that the most used methods in the analyzed works are deep learning-based (70 papers), while fewer works employ classic computer vision only (101 papers). The most recurrent metric used for classification and object detection was the accuracy (33 papers and 5 papers), while for segmentation it was the Dice Similarity Coefficient (38 papers). Regarding staining techniques, Papanicolaou was the most employed one (130 papers), followed by H&E (20 papers) and Feulgen (5 papers). Twelve of the datasets used in the papers are publicly available, with the DTU/Herlev dataset being the most used one. We conclude that there still is a lack of high-quality datasets for many types of stains and most of the works are not mature enough to be applied in a daily clinical diagnostic routine. We also identified a growing tendency towards adopting deep learning-based approaches as the methods of choice.

Aneuploidy detection for diagnostic and prognostic use in premalignant and malignant lesions of the uterine cervix: A systematic review.

OBJECTIVE: To systematically review the role of aneuploidy detection alone or in combination with other methods in cervical cancer screening and to evaluate the value of aneuploidy to predict the behavior of premalignant cervical lesions. METHOD: We conducted a systematic review based on an electronic search for articles published between 2001 and 2020 across databases including MEDLINE/PubMed, Scopus, and Web of Science. Studies were subjected to data extraction, risk of bias, and narrative synthesis. RESULTS: A total of 15 articles were included in the review. Eight out of 15 studies (53.3%) were judged to be at a high or unclear risk of bias. From the 15 included studies, the index test to detect aneuploidy was DNA image cytometry (DNA-ICM) in 12 studies and DNA flow cytometry (DNA-FCM) in three studies. Nine studies also evaluated the performance of cytology and/or human papillomavirus (HPV) tests. For DNA-ICM, sensitivity to detect cervical intraepithelial neoplasia or worse (CIN2+) varied between 59.0% and 95.9% and specificity varied between 54.1% and 100%. For DNA-FCM, sensitivity varied between 27.3% to 96.8% and specificity was 100%. For cytological evaluation, sensitivity varied between 25.0% and 70.4% and specificity varied between 70.6% and 99.9%. For HPV detection, sensitivity varied between 39.4% and 100% and specificity varied between 23.3% and 84.3%. CONCLUSION: DNA ploidy along with atypical cells findings in cytology and/or HPV detection revealed great value to detect CIN2+ lesions and to predict which lesions are more likely to progress to cervical cancer.