Colposcopy Accuracy and Diagnostic Performance: A Quality Control and Quality Assurance Survey in Italian Tertiary-Level Teaching and Academic Institutions—The Italian Society of Colposcopy and Cervico-Vaginal Pathology (SICPCV)

Origoni, Massimo; Cantatore, Francesco; Sopracordevole, Francesco; Clemente, Nicolò; Spinillo, Arsenio; Gardella, Barbara; Vincenzo, Rosa De; Ricci, Caterina; Landoni, Fabio; Meo, Maria Letizia Di; Ciavattini, Andrea; Giuseppe, Jacopo Di; Preti, Eleonora Petra; Iacobone, Anna Daniela; Carriero, Carmine; Dellino, Miriam; Capodanno, M.; Perino, Antonio; Miglioli, Cesare; Insolia, Luca; Barbero, Maggiorino; Candiani, Massimo

doi:10.3390/diagnostics13111906

Cited by 3 publications

(4 citation statements)

References 38 publications

(52 reference statements)

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“…For the calculations presented in this work, the false-negative rate is within 7%. This is substantially better than the modern invasive colposcopy tests [21], which miss cancer cases at 27%. It can be improved even further by combining several cells for diagnosis.…”

Section: Resultsmentioning

confidence: 88%

“…It has to be stressed that the increase observed in sensitivity while keeping the number of missed cancers (false negatives) low is the currently existing unmet need in clinical practice. With a sensitivity rate of 92%, the percentage of missed cancer cases (false negative rate) is only 7%, which is much better compared with the 27% achieved with modern invasive colposcopy tests [21]. The analysis is applied to the adhesion maps collected on six primary precancerous and six primary cancerous cell lines; each cell line was extracted from a different patient.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Allows for Distinguishing Precancerous and Cancerous Human Epithelial Cervical Cells Using High-Resolution AFM Imaging of Adhesion Maps

Petrov,

Sokolov

2023

Cells

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Previously, the analysis of atomic force microscopy (AFM) images allowed us to distinguish normal from cancerous/precancerous human epithelial cervical cells using only the fractal dimension parameter. High-resolution maps of adhesion between the AFM probe and the cell surface were used in that study. However, the separation of cancerous and precancerous cells was rather poor (the area under the curve (AUC) was only 0.79, whereas the accuracy, sensitivity, and specificity were 74%, 58%, and 84%, respectively). At the same time, the separation between premalignant and malignant cells is the most significant from a clinical point of view. Here, we show that the introduction of machine learning methods for the analysis of adhesion maps allows us to distinguish precancerous and cancerous cervical cells with rather good precision (AUC, accuracy, sensitivity, and specificity are 0.93, 83%, 92%, and 78%, respectively). Substantial improvement in sensitivity is significant because of the unmet need in clinical practice to improve the screening of cervical cancer (a relatively low specificity can be compensated by combining this approach with other currently existing screening methods). The random forest decision tree algorithm was utilized in this study. The analysis was carried out using the data of six precancerous primary cell lines and six cancerous primary cell lines, each derived from different humans. The robustness of the classification was verified using K-fold cross-validation (K = 500). The results are statistically significant at p < 0.0001. Statistical significance was determined using the random shuffle method as a control.

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Section: Resultsmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Machine Learning Allows for Distinguishing Precancerous and Cancerous Human Epithelial Cervical Cells Using High-Resolution AFM Imaging of Adhesion Maps

Petrov,

Sokolov

2023

Cells

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“…Currently, the screening methods for these types of tumors are performed through the cytopathological Papanicolaou (Pap) Smear test which has a high specificity (>90%), although the sensibility varies from 74 to 98%; High Risk-Human Papillomavirus (HPV-HR) test, with 97–99% sensibility and 84–89% specificity; and colposcopy, which has 90% sensibility and 35–50% specificity [ 14 , 15 , 16 , 17 ]. However, there is apprehension regarding the precision of the aforementioned methods and their subsequent efficacy in facilitating precise screening and early diagnosis of cervix cancer [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Cervical Squamous Cell Carcinoma Diagnosis by FTIR Microspectroscopy

Félix,

Tavares,

Santos

et al. 2024

Molecules

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Cervical cancer was considered the fourth most common cancer worldwide in 2020. In order to reduce mortality, an early diagnosis of the tumor is required. Currently, this type of cancer occurs mostly in developing countries due to the lack of vaccination and screening against the Human Papillomavirus. Thus, there is an urgent clinical need for new methods aiming at a reliable screening and an early diagnosis of precancerous and cancerous cervical lesions. Vibrational spectroscopy has provided very good results regarding the diagnosis of various tumors, particularly using Fourier transform infrared microspectroscopy, which has proved to be a promising complement to the currently used histopathological methods of cancer diagnosis. This spectroscopic technique was applied to the analysis of cryopreserved human cervical tissue samples, both squamous cell carcinoma (SCC) and non-cancer samples. A dedicated Support Vector Machine classification model was constructed in order to categorize the samples into either normal or malignant and was subsequently validated by cross-validation, with an accuracy higher than 90%.

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“…This study emphasizes the significance of educating new generations of skilled gynecologists and standardizing a procedure that is inherently subjective. The adoption of colposcopy standards and quality recommendations by scientific societies is a fundamental step towards ensuring effective cervical cancer prevention [11].…”

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confidence: 99%