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Pisano L, Giachini C, Turco M, Farini J, Caminati F, Giani I, Elbetti C, Bisanzi S, Cannistrà S, Pompeo G, Sani C, Pimpinelli N. PAP-HPV Co-Testing in Anal Cancer Screening: An Italian Experience. J Clin Med 2025; 14:2186. [PMID: 40217637 PMCID: PMC11989535 DOI: 10.3390/jcm14072186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2025] [Revised: 03/16/2025] [Accepted: 03/20/2025] [Indexed: 04/14/2025] Open
Abstract
Background/Objectives: Squamous cell carcinoma of the anus (SCCA) remains a relatively rare form of cancer linked to high-risk human papillomavirus (HR-HPV) infection; however, its incidence has been increasing globally. Anal cytology and HR-HPV testing can identify precursors, though standardized screening guidelines are still lacking. This study aimed to assess the correlation between high-resolution anoscopy (HRA) findings and primary screening results through PAP-HPV co-testing in high-risk patients. Methods: A retrospective, single-center study was conducted collecting data from the joint multidisciplinary anal cancer clinic of Piero Palagi Hospital in Florence (Italy), between August 2019 and September 2022. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of anal cytology, HR-HPV testing, and PAP-HPV co-testing were assessed. Results: In 577 HRAs, histology revealed 31 AIN2+ lesions (5.4%) and 220 AIN1 lesions (38.1%), while 326 (56.5%) were negative. Cytology alone showed a sensitivity of 74.2% and specificity of 63.3% for AIN2+ lesions, while HR-HPV testing alone had a sensitivity of 96.8% and specificity of 38.1%. Co-testing demonstrated 100% sensitivity and a 100% NPV for AIN2+ lesions. Among men who have sex with men (MSM), no significant differences in outcomes were observed between HIV-positive and HIV-negative patients, likely reflecting similar high-risk behaviors and effective HIV treatments. Conclusions: Co-testing with anal cytology and HR-HPV testing provides the most reliable screening for high-grade lesions (AIN2+), surpassing the reliability of individual methods. Tailored co-testing strategies are crucial for early detection and effective prevention in high-risk groups.
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Affiliation(s)
- Luigi Pisano
- Section of Dermatology, Health Sciences Department, University of Florence, 50121 Florence, Italy; (L.P.); (J.F.); (N.P.)
| | - Claudia Giachini
- Regional Laboratory of Cancer Prevention, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50121 Florence, Italy; (C.G.); (S.B.); (S.C.); (G.P.); (C.S.)
| | - Martina Turco
- Section of Dermatology, Health Sciences Department, University of Florence, 50121 Florence, Italy; (L.P.); (J.F.); (N.P.)
| | - Jacopo Farini
- Section of Dermatology, Health Sciences Department, University of Florence, 50121 Florence, Italy; (L.P.); (J.F.); (N.P.)
| | - Filippo Caminati
- SOSD Proctologia, USL Toscana Centro, 50121 Florence, Italy; (F.C.); (I.G.); (C.E.)
| | - Iacopo Giani
- SOSD Proctologia, USL Toscana Centro, 50121 Florence, Italy; (F.C.); (I.G.); (C.E.)
| | - Claudio Elbetti
- SOSD Proctologia, USL Toscana Centro, 50121 Florence, Italy; (F.C.); (I.G.); (C.E.)
| | - Simonetta Bisanzi
- Regional Laboratory of Cancer Prevention, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50121 Florence, Italy; (C.G.); (S.B.); (S.C.); (G.P.); (C.S.)
| | - Stefania Cannistrà
- Regional Laboratory of Cancer Prevention, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50121 Florence, Italy; (C.G.); (S.B.); (S.C.); (G.P.); (C.S.)
| | - Giampaolo Pompeo
- Regional Laboratory of Cancer Prevention, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50121 Florence, Italy; (C.G.); (S.B.); (S.C.); (G.P.); (C.S.)
| | - Cristina Sani
- Regional Laboratory of Cancer Prevention, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50121 Florence, Italy; (C.G.); (S.B.); (S.C.); (G.P.); (C.S.)
| | - Nicola Pimpinelli
- Section of Dermatology, Health Sciences Department, University of Florence, 50121 Florence, Italy; (L.P.); (J.F.); (N.P.)
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Mota J, Almeida MJ, Martins M, Mendes F, Cardoso P, Afonso J, Ribeiro T, Ferreira J, Fonseca F, Limbert M, Lopes S, Macedo G, Castro Poças F, Mascarenhas M. Artificial Intelligence in Coloproctology: A Review of Emerging Technologies and Clinical Applications. J Clin Med 2024; 13:5842. [PMID: 39407902 PMCID: PMC11477032 DOI: 10.3390/jcm13195842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2024] [Revised: 09/21/2024] [Accepted: 09/22/2024] [Indexed: 10/20/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a transformative tool across several specialties, namely gastroenterology, where it has the potential to optimize both diagnosis and treatment as well as enhance patient care. Coloproctology, due to its highly prevalent pathologies and tremendous potential to cause significant mortality and morbidity, has drawn a lot of attention regarding AI applications. In fact, its application has yielded impressive outcomes in various domains, colonoscopy being one prominent example, where it aids in the detection of polyps and early signs of colorectal cancer with high accuracy and efficiency. With a less explored path but equivalent promise, AI-powered capsule endoscopy ensures accurate and time-efficient video readings, already detecting a wide spectrum of anomalies. High-resolution anoscopy is an area that has been growing in interest in recent years, with efforts being made to integrate AI. There are other areas, such as functional studies, that are currently in the early stages, but evidence is expected to emerge soon. According to the current state of research, AI is anticipated to empower gastroenterologists in the decision-making process, paving the way for a more precise approach to diagnosing and treating patients. This review aims to provide the state-of-the-art use of AI in coloproctology while also reflecting on future directions and perspectives.
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Affiliation(s)
- Joana Mota
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Maria João Almeida
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Miguel Martins
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Francisco Mendes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Pedro Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - João Afonso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering, University of Porto, 4200-065 Porto, Portugal;
- DigestAID—Digestive Artificial Intelligence Development, Rua Alfredo Allen n.° 455/461, 4200-135 Porto, Portugal
| | - Filipa Fonseca
- Instituto Português de Oncologia de Lisboa Francisco Gentil (IPO Lisboa), 1099-023 Lisboa, Portugal; (F.F.); (M.L.)
| | - Manuel Limbert
- Instituto Português de Oncologia de Lisboa Francisco Gentil (IPO Lisboa), 1099-023 Lisboa, Portugal; (F.F.); (M.L.)
- Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal;
| | - Susana Lopes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal;
- Faculty of Medicine, University of Porto, 4200-047 Porto, Portugal
| | - Guilherme Macedo
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-047 Porto, Portugal
| | - Fernando Castro Poças
- Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal;
- Department of Gastroenterology, Santo António University Hospital, 4099-001 Porto, Portugal
- Abel Salazar Biomedical Sciences Institute (ICBAS), 4050-313 Porto, Portugal
| | - Miguel Mascarenhas
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal;
- Faculty of Medicine, University of Porto, 4200-047 Porto, Portugal
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Saraiva MM, Spindler L, Manzione T, Ribeiro T, Fathallah N, Martins M, Cardoso P, Mendes F, Fernandes J, Ferreira J, Macedo G, Nadal S, de Parades V. Deep Learning and High-Resolution Anoscopy: Development of an Interoperable Algorithm for the Detection and Differentiation of Anal Squamous Cell Carcinoma Precursors-A Multicentric Study. Cancers (Basel) 2024; 16:1909. [PMID: 38791987 PMCID: PMC11119426 DOI: 10.3390/cancers16101909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 05/04/2024] [Accepted: 05/06/2024] [Indexed: 05/26/2024] Open
Abstract
High-resolution anoscopy (HRA) plays a central role in the detection and treatment of precursors of anal squamous cell carcinoma (ASCC). Artificial intelligence (AI) algorithms have shown high levels of efficiency in detecting and differentiating HSIL from low-grade squamous intraepithelial lesions (LSIL) in HRA images. Our aim was to develop a deep learning system for the automatic detection and differentiation of HSIL versus LSIL using HRA images from both conventional and digital proctoscopes. A convolutional neural network (CNN) was developed based on 151 HRA exams performed at two volume centers using conventional and digital HRA systems. A total of 57,822 images were included, 28,874 images containing HSIL and 28,948 LSIL. Partial subanalyses were performed to evaluate the performance of the CNN in the subset of images acetic acid and lugol iodine staining and after treatment of the anal canal. The overall accuracy of the CNN in distinguishing HSIL from LSIL during the testing stage was 94.6%. The algorithm had an overall sensitivity and specificity of 93.6% and 95.7%, respectively (AUC 0.97). For staining with acetic acid, HSIL was differentiated from LSIL with an overall accuracy of 96.4%, while for lugol and after therapeutic manipulation, these values were 96.6% and 99.3%, respectively. The introduction of AI algorithms to HRA may enhance the early diagnosis of ASCC precursors, and this system was shown to perform adequately across conventional and digital HRA interfaces.
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Affiliation(s)
- Miguel Mascarenhas Saraiva
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (M.M.); (P.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Lucas Spindler
- Department of Proctology, GH Paris Saint-Joseph, 185, Rue Raymond Losserand, 75014 Paris, France
| | - Thiago Manzione
- Department of Surgery, Instituto de Infectologia Emílio Ribas, São Paulo 01246-900, Brazil
| | - Tiago Ribeiro
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (M.M.); (P.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Nadia Fathallah
- Department of Proctology, GH Paris Saint-Joseph, 185, Rue Raymond Losserand, 75014 Paris, France
| | - Miguel Martins
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (M.M.); (P.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Pedro Cardoso
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (M.M.); (P.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Francisco Mendes
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (M.M.); (P.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Joana Fernandes
- Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
- DigestAID—Artificial Intelligence Development, Rua Alfredo Allen, 4200-135 Porto, Portugal
| | - João Ferreira
- Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
- DigestAID—Artificial Intelligence Development, Rua Alfredo Allen, 4200-135 Porto, Portugal
| | - Guilherme Macedo
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (T.R.); (M.M.); (P.C.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Sidney Nadal
- Department of Surgery, Instituto de Infectologia Emílio Ribas, São Paulo 01246-900, Brazil
| | - Vincent de Parades
- Department of Proctology, GH Paris Saint-Joseph, 185, Rue Raymond Losserand, 75014 Paris, France
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Mascarenhas Saraiva M, Spindler L, Fathallah N, Beaussier H, Mamma C, Ribeiro T, Afonso J, Carvalho M, Moura R, Cardoso P, Mendes F, Martins M, Adam J, Ferreira J, Macedo G, de Parades V. Deep Learning in High-Resolution Anoscopy: Assessing the Impact of Staining and Therapeutic Manipulation on Automated Detection of Anal Cancer Precursors. Clin Transl Gastroenterol 2024; 15:e00681. [PMID: 38270249 PMCID: PMC11042766 DOI: 10.14309/ctg.0000000000000681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 01/10/2024] [Indexed: 01/26/2024] Open
Abstract
INTRODUCTION High-resolution anoscopy (HRA) is the gold standard for detecting anal squamous cell carcinoma (ASCC) precursors. Preliminary studies on the application of artificial intelligence (AI) models to this modality have revealed promising results. However, the impact of staining techniques and anal manipulation on the effectiveness of these algorithms has not been evaluated. We aimed to develop a deep learning system for automatic differentiation of high-grade squamous intraepithelial lesion vs low-grade squamous intraepithelial lesion in HRA images in different subsets of patients (nonstained, acetic acid, lugol, and after manipulation). METHODS A convolutional neural network was developed to detect and differentiate high-grade and low-grade anal squamous intraepithelial lesions based on 27,770 images from 103 HRA examinations performed in 88 patients. Subanalyses were performed to evaluate the algorithm's performance in subsets of images without staining, acetic acid, lugol, and after manipulation of the anal canal. The sensitivity, specificity, accuracy, positive and negative predictive values, and area under the curve were calculated. RESULTS The convolutional neural network achieved an overall accuracy of 98.3%. The algorithm had a sensitivity and specificity of 97.4% and 99.2%, respectively. The accuracy of the algorithm for differentiating high-grade squamous intraepithelial lesion vs low-grade squamous intraepithelial lesion varied between 91.5% (postmanipulation) and 100% (lugol) for the categories at subanalysis. The area under the curve ranged between 0.95 and 1.00. DISCUSSION The introduction of AI to HRA may provide an accurate detection and differentiation of ASCC precursors. Our algorithm showed excellent performance at different staining settings. This is extremely important because real-time AI models during HRA examinations can help guide local treatment or detect relapsing disease.
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Affiliation(s)
- Miguel Mascarenhas Saraiva
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, Porto, Portugal
| | - Lucas Spindler
- Department of Proctology, GH Paris Saint-Joseph, Paris, France
| | - Nadia Fathallah
- Department of Proctology, GH Paris Saint-Joseph, Paris, France
| | - Hélene Beaussier
- Department of Clinical Research, GH Paris Saint-Joseph, Paris, France
| | - Célia Mamma
- Department of Clinical Research, GH Paris Saint-Joseph, Paris, France
| | - Tiago Ribeiro
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - João Afonso
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Mariana Carvalho
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
- INEGI—Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Rita Moura
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
- INEGI—Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Pedro Cardoso
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Francisco Mendes
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Miguel Martins
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Julien Adam
- Department of Pathology, GH Paris Saint-Joseph, Paris, France
| | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
- INEGI—Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Guilherme Macedo
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, Porto, Portugal
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Saraiva MM, Spindler L, Fathallah N, Beaussier H, Mamma C, Quesnée M, Ribeiro T, Afonso J, Carvalho M, Moura R, Andrade P, Cardoso H, Adam J, Ferreira J, Macedo G, de Parades V. Artificial intelligence and high-resolution anoscopy: automatic identification of anal squamous cell carcinoma precursors using a convolutional neural network. Tech Coloproctol 2022; 26:893-900. [DOI: 10.1007/s10151-022-02684-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 08/09/2022] [Indexed: 10/15/2022]
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New screening strategy combining anal PAP and HPV tests for HPV-related anal cancer: a prospective, single center study. Sex Transm Dis 2022; 49:622-627. [PMID: 35687884 DOI: 10.1097/olq.0000000000001660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE The objective of this study was to evaluate the performance of a combined approach of liquid-based anal cytology and HPV testing in predicting patients who should undergo high-resolution anoscopy (HRA) for the early detection of anal cancer and anal intraepithelial neoplasia (AIN)-2 +. METHODS Prospective single-center quality improvement study. We consecutively enrolled men who had sex with men (MSM) attending our sexually transmitted disease clinic to undergo anal Pap and HPV tests. All patients with an abnormal anal Pap test result and/or positive HPV test result underwent HRA. RESULTS We enrolled 217 MSM, 80 HIV-positive patients, and 137 HIV-negative patients. Cytology showed a sensitivity of 100%, specificity of 64,1%, accuracy of 66.7%, positive predictive value (PPV) of 15,7% and negative predictive value (NPV) of 100% for the detection of AIN-2+. The high-risk (HR)-HPV test showed a sensitivity, specificity, accuracy, PPV, and NPV of 100%, 36.4%, 40%, 9.4%, and 100%, respectively. The combination of abnormal cytology with identification of infection by at least one HR-HPV strain on the HPV test had a sensitivity of 100%, specificity of 73%, accuracy of 74.6%, PPV of 19.1%, and NPV of 100%. CONCLUSION Anal HR-HPV testing, complementary to cytology, improves the diagnostic accuracy of screening for anal cancer.
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