TY - JOUR
T1 - Cervical lesion assessment using real-time microendoscopy image analysis in Brazil
T2 - The CLARA study
AU - Hunt, Brady
AU - Fregnani, José Humberto Tavares Guerreiro
AU - Brenes, David
AU - Schwarz, Richard A.
AU - Salcedo, Mila P.
AU - Possati-Resende, Júlio César
AU - Antoniazzi, Márcio
AU - de Oliveira Fonseca, Bruno
AU - Santana, Iara Viana Vidigal
AU - de Macêdo Matsushita, Graziela
AU - Castle, Philip E.
AU - Schmeler, Kathleen M.
AU - Richards-Kortum, Rebecca
N1 - Publisher Copyright:
© 2021 UICC
PY - 2021/7/15
Y1 - 2021/7/15
N2 - We conducted a prospective evaluation of the diagnostic performance of high-resolution microendoscopy (HRME) to detect cervical intraepithelial neoplasia (CIN) in women with abnormal screening tests. Study participants underwent colposcopy, HRME and cervical biopsy. The prospective diagnostic performance of HRME using an automated morphologic image analysis algorithm was compared to that of colposcopy using histopathologic detection of CIN as the gold standard. To assess the potential to further improve performance of HRME image analysis, we also conducted a retrospective analysis assessing performance of a multi-task convolutional neural network to segment and classify HRME images. One thousand four hundred eighty-six subjects completed the study; 435 (29%) subjects had CIN Grade 2 or more severe (CIN2+) diagnosis. HRME with morphologic image analysis for detection of CIN Grade 3 or more severe diagnoses (CIN3+) was similarly sensitive (95.6% vs 96.2%, P =.81) and specific (56.6% vs 58.7%, P =.18) as colposcopy. HRME with morphologic image analysis for detection of CIN2+ was slightly less sensitive (91.7% vs 95.6%, P <.01) and specific (59.7% vs 63.4%, P =.02) than colposcopy. Images from 870 subjects were used to train a multi-task convolutional neural network-based algorithm and images from the remaining 616 were used to validate its performance. There were no significant differences in the sensitivity and specificity of HRME with neural network analysis vs colposcopy for detection of CIN2+ or CIN3+. Using a neural network-based algorithm, HRME has comparable sensitivity and specificity to colposcopy for detection of CIN2+. HRME could provide a low-cost, point-of-care alternative to colposcopy and biopsy in the prevention of cervical cancer.
AB - We conducted a prospective evaluation of the diagnostic performance of high-resolution microendoscopy (HRME) to detect cervical intraepithelial neoplasia (CIN) in women with abnormal screening tests. Study participants underwent colposcopy, HRME and cervical biopsy. The prospective diagnostic performance of HRME using an automated morphologic image analysis algorithm was compared to that of colposcopy using histopathologic detection of CIN as the gold standard. To assess the potential to further improve performance of HRME image analysis, we also conducted a retrospective analysis assessing performance of a multi-task convolutional neural network to segment and classify HRME images. One thousand four hundred eighty-six subjects completed the study; 435 (29%) subjects had CIN Grade 2 or more severe (CIN2+) diagnosis. HRME with morphologic image analysis for detection of CIN Grade 3 or more severe diagnoses (CIN3+) was similarly sensitive (95.6% vs 96.2%, P =.81) and specific (56.6% vs 58.7%, P =.18) as colposcopy. HRME with morphologic image analysis for detection of CIN2+ was slightly less sensitive (91.7% vs 95.6%, P <.01) and specific (59.7% vs 63.4%, P =.02) than colposcopy. Images from 870 subjects were used to train a multi-task convolutional neural network-based algorithm and images from the remaining 616 were used to validate its performance. There were no significant differences in the sensitivity and specificity of HRME with neural network analysis vs colposcopy for detection of CIN2+ or CIN3+. Using a neural network-based algorithm, HRME has comparable sensitivity and specificity to colposcopy for detection of CIN2+. HRME could provide a low-cost, point-of-care alternative to colposcopy and biopsy in the prevention of cervical cancer.
KW - cervical cancer prevention
KW - deep learning
KW - diagnostic imaging
KW - high-resolution microendoscopy
KW - point-of-care
UR - http://www.scopus.com/inward/record.url?scp=85103905433&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85103905433&partnerID=8YFLogxK
U2 - 10.1002/ijc.33543
DO - 10.1002/ijc.33543
M3 - Article
C2 - 33811763
AN - SCOPUS:85103905433
SN - 0020-7136
VL - 149
SP - 431
EP - 441
JO - International journal of cancer
JF - International journal of cancer
IS - 2
ER -