Endocervical curettage for diagnosing high-grade squamous intraepithelial lesions or worse in women with type 3 transformation zone lesions: a retrospective, observational study.

BACKGROUND: This study aimed to assess the value of endocervical curettage (ECC) in detecting high-grade squamous intraepithelial lesion or worse (HSIL+) in women with type 3 transformation zone (TZ3) lesions, and to identify the clinical characteristics of patients with TZ3 lesions who benefit most from ECC. METHODS: This retrospective, multicenter study included 1,905 women with TZ3 lesions who attended cervical screening in one of seven tertiary hospitals in China between January 2020 and November 2021. All participants had received abnormal results and had been referred to colposcopy. Risk factors were identified through univariate and multifactorial logistic analyses. RESULTS: In total, 20.5% (n = 391) of HSIL+ cases with TZ3 lesions had been diagnosed with biopsy and ECC. ECC detected 0.8% (n = 15) HSIL+ cases otherwise missed by biopsy alone. Multivariate analysis identified four factors which influenced detection performance. The probability of detecting HSIL+ with ECC is 2.653 (95% confidence interval [CI] 1.009-6.977) times greater in women aged 40-49 years and 2.545 (95% CI 0.965-6.716) times greater for those aged 50 years and older compared to those younger than 30 years. The probability of ASC-H (atypical squamous cells, cannot exclude high-grade squamous intraepithelial lesion) and HSIL cytologies were respectively 2.415 (95% CI 1.213-4.808) and 2.933 (95% CI 1.648-5.220) times higher than for NILM (negative for intraepithelial lesion or malignancy). Women with human papillomavirus 16/18 infections were 2.299 (95% CI 0.942-5.613) times more likely to be HSIL+. Precancerous lesions were 35.884 (95% CI 12.214-105.426) times more likely in women who had high-grade colposcopic impressions compared to those with normal impressions. CONCLUSIONS: ECC should be performed for patients with ASC-H or HSIL cytologies, human papillomavirus 16/18 infections, and for those with high-grade colposcopic impressions. This will increase the number of HSIL+ cases identified using biopsy by reducing the number of false negatives.

Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis.

BACKGROUND: A number of publications have demonstrated that deep learning (DL) algorithms matched or outperformed clinicians in image-based cancer diagnostics, but these algorithms are frequently considered as opponents rather than partners. Despite the clinicians-in-the-loop DL approach having great potential, no study has systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification. OBJECTIVE: We systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification. METHODS: PubMed, Embase, IEEEXplore, and the Cochrane Library were searched for studies published between January 1, 2012, and December 7, 2021. Any type of study design was permitted that focused on comparing unassisted clinicians and DL-assisted clinicians in cancer identification using medical imaging. Studies using medical waveform-data graphics material and those investigating image segmentation rather than classification were excluded. Studies providing binary diagnostic accuracy data and contingency tables were included for further meta-analysis. Two subgroups were defined and analyzed, including cancer type and imaging modality. RESULTS: In total, 9796 studies were identified, of which 48 were deemed eligible for systematic review. Twenty-five of these studies made comparisons between unassisted clinicians and DL-assisted clinicians and provided sufficient data for statistical synthesis. We found a pooled sensitivity of 83% (95% CI 80%-86%) for unassisted clinicians and 88% (95% CI 86%-90%) for DL-assisted clinicians. Pooled specificity was 86% (95% CI 83%-88%) for unassisted clinicians and 88% (95% CI 85%-90%) for DL-assisted clinicians. The pooled sensitivity and specificity values for DL-assisted clinicians were higher than for unassisted clinicians, at ratios of 1.07 (95% CI 1.05-1.09) and 1.03 (95% CI 1.02-1.05), respectively. Similar diagnostic performance by DL-assisted clinicians was also observed across the predefined subgroups. CONCLUSIONS: The diagnostic performance of DL-assisted clinicians appears better than unassisted clinicians in image-based cancer identification. However, caution should be exercised, because the evidence provided in the reviewed studies does not cover all the minutiae involved in real-world clinical practice. Combining qualitative insights from clinical practice with data-science approaches may improve DL-assisted practice, although further research is required. TRIAL REGISTRATION: PROSPERO CRD42021281372; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=281372.

Correction: Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis.

[This corrects the article DOI: 10.2196/43832.].

Improving colposcopic accuracy for cervical precancer detection: a retrospective multicenter study in China.

BACKGROUND: Colposcopy alone can result in misidentification of high-grade squamous intraepithelial or worse lesions (HSIL +), especially for women with Type 3 transformation zone (TZ) lesions, where colposcopic assessment is particularly imprecise. This study aimed to improve HSIL + case identification by supplementing referral screening results to colposcopic findings. METHODS: This is an observational multicenter study of 2,417 women, referred to colposcopy after receiving cervical cancer screening results. Logistic regression analysis was conducted under uni- and multivariate models to identify factors which could be used to improve HSIL + case identification. Histological diagnosis was established as the gold standard and is used to assess accuracy, sensitivity, and specificity, as well as to incrementally improve colposcopy. RESULTS: Multivariate analysis highlighted age, TZ types, referral screening, and colposcopists' skills as independent factors. Across this sample population, diagnostic accuracies for detecting HSIL + increased from 72.9% (95%CI 71.1-74.7%) for colposcopy alone to 82.1% (95%CI 80.6-83.6%) after supplementing colposcopy with screening results. A significant increase in colposcopic accuracy was observed across all subgroups. Although, the highest increase was observed in women with a TZ3 lesion, and for those diagnosed by junior colposcopists. CONCLUSION: It appears possible to supplement colposcopic examinations with screening results to improve HSIL + detection, especially for women with TZ3 lesions. It may also be possible to improve junior colposcopists' diagnoses although, further psychological research is necessary. We need to understand how levels of uncertainty influence diagnostic decisions and what the concept of "experience" actually is and what it means for colposcopic practice.

Development and validation of a predictive model for endocervical curettage in patients referred for colposcopy: A multicenter retrospective diagnostic study in China.

Objective: This study aimed to develop a nomogram that can predict occult high-grade squamous intraepithelial lesions or worse (HSIL+) and determine the need for endocervical curettage (ECC) in patients referred for colposcopy. Methods: This retrospective multicenter study included 4,149 patients who were referred to any one of six tertiary hospitals in China for colposcopy between January 2020 and November 2021 because of abnormal screening results. ECC data were extracted from the medical records. Univariate and multivariate logistic regression analyses were performed to identify factors that could predict HSIL+ on ECC. Patients were randomly assigned to a training set or to an internal validation set for performance and comparability testing. The model was externally validated and tested in patients from two additional hospitals. The nomogram was assessed in terms of discrimination and calibration and subjected to decision curve analysis. Results: HSIL+ was found on ECC in 38.8% (n=388) of cases. Our predictive nomogram included age group, cytology, human papillomavirus (HPV) status, visibility of the cervix and colposcopic impression. The nomogram had good overall discrimination, which was internally validated [area under the receiver-operator characteristic (AUC), 0.839; 95% confidence interval (95% CI), 0.773-0.904]. In terms of external validation, the AUC was 0.843 (95% CI, 0.773-0.912) for the consecutive sample and 0.843 (95% CI, 0.783-0.902) for the comparative sample. Calibration analysis suggested good consistency between predicted and observed probabilities. Decision curve analysis suggested this nomogram would be clinically useful with almost the entire range of threshold probabilities. Conclusions: This internally and externally validated nomogram can be easily applied and incorporates multiple clinically relevant variables that can be used to identify patients with occult HSIL+ who need ECC.