Search

ObjectivesWe investigated HPV genotypes in a large cohort of patients with definitive cervical histologic diagnosis.MethodsHPV testing was performed by real-time PCR assay, including 18 high-risk HPV (hrHPV) and 3 low-risk HPV (lrHPV). Totally 61,422 patients with documented HPV genotyping results within 6 months before cervical histologic diagnoses were included.ResultsHrHPV positive rate was 55.1% among all tested cases with the highest in adenosquamous carcinoma (94.1%), followed by squamous cell carcinoma (SCC) (93.7%), cervical intraepithelial neoplasia 2/3 (CIN2/3) (92.8%). HrHPV positive rates were significantly higher in high-grade squamous lesions than in those in glandular lesions. HPV16 was the most common genotype followed by HPV52 and HPV58 in CIN2/3. The most frequent hrHPV genotype in adenocarcinoma in situ (AIS) was HPV18, followed by HPV16, HPV45 and HPV52. In SCC cases, HPV16 was the most common type followed by HPV58, HPV52, HPV18 and HPV33. However, HPV18 showed significantly higher prevalence in adenocarcinoma and adenosquamous carcinoma than in that in SCC. Theoretically, the protective rates of 2/4-valent and 9-valent vaccine were 69.1% and 85.8% for cervical cancers.ConclusionsThe prevalence of HPV genotypes in Chinese population was different from that in Western population. Some hrHPV types were identified in cervical precancerous lesions and cancers, which are not included in current HPV vaccines. These data provide baseline knowledge for future HPV vaccine development.

ObjectivesTriple-negative breast cancer (TNBC) is a heterogeneous disease, and different histological subtypes of TNBC have different clinicopathological features and prognoses. Therefore, this study aimed to establish a nomogram model to predict the histological heterogeneity of TNBC: including Metaplastic Carcinoma (MC) and Non-Metaplastic Carcinoma (NMC).MethodsWe evaluated 117 patients who had pathologically confirmed TNBC between November 2016 and December 2020 and collected preoperative multiparameter MRI and clinicopathological data. The patients were randomly assigned to a training set and a validation set at a ratio of 3:1. Based on logistic regression analysis, we established a nomogram model to predict the histopathological subtype of TNBC. Nomogram performance was assessed with the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve. According to the follow-up information, disease-free survival (DFS) survival curve was estimated using the Kaplan-Meier product-limit method.ResultsOf the 117 TNBC patients, 29 patients had TNBC-MC (age range, 29–65 years; median age, 48.0 years), and 88 had TNBC-NMC (age range, 28–88 years; median age, 44.5 years). Multivariate logistic regression analysis demonstrated that lesion type (p = 0.001) and internal enhancement pattern (p = 0.001) were significantly predictive of TNBC subtypes in the training set. The nomogram incorporating these variables showed excellent discrimination power with an AUC of 0.849 (95% CI: 0.750−0.949) in the training set and 0.819 (95% CI: 0.693−0.946) in the validation set. Up to the cutoff date for this analysis, a total of 66 patients were enrolled in the prognostic analysis. Six of 14 TNBC-MC patients experienced recurrence, while 7 of 52 TNBC-NMC patients experienced recurrence. The DFS of the two subtypes was significantly different (p=0.035).ConclusionsIn conclusion, we developed a nomogram consisting of lesion type and internal enhancement pattern, which showed good discrimination ability in predicting TNBC-MC and TNBC-NMC.

PurposeTo assess whether apparent diffusion coefficient (ADC) metrics can be used to assess tumor-infiltrating lymphocyte (TIL) levels in breast cancer, particularly in the molecular subtypes of breast cancer.MethodsIn total, 114 patients with breast cancer met the inclusion criteria (mean age: 52 years; range: 29–85 years) and underwent multi-parametric breast magnetic resonance imaging (MRI). The patients were imaged by diffusion-weighted (DW)-MRI (1.5 T) using a single-shot spin-echo echo-planar imaging sequence. Two readers independently drew a region of interest (ROI) on the ADC maps of the whole tumor. The mean ADC and histogram parameters (10th, 25th, 50th, 75th, and 90th percentiles of ADC, skewness, entropy, and kurtosis) were used as features to analyze associations with the TIL levels in breast cancer. Additionally, the correlation between the ADC values and Ki-67 expression were analyzed. Continuous variables were compared with Student’s t-test or Mann-Whitney U test if the variables were not normally distributed. Categorical variables were compared using Pearson’s chi-square test or Fisher’s exact test. Associations between TIL levels and imaging features were evaluated by the Mann-Whitney U and Kruskal-Wallis tests.ResultsA statistically significant difference existed in the 10th and 25th percentile ADC values between the low and high TIL groups in breast cancer (P=0.012 and 0.027). For the luminal subtype of breast cancer, the 10th percentile ADC value was significantly lower in the low TIL group (P=0.041); for the non-luminal subtype of breast cancer, the kurtosis was significantly lower in the low TIL group (P=0.023). The Ki-67 index showed statistical significance for evaluating the TIL levels in breast cancer (P=0.007). Additionally, the skewness was significantly higher for samples with high Ki-67 levels in breast cancer (P=0.029).ConclusionsOur findings suggest that whole-lesion ADC histogram parameters can be used as surrogate biomarkers to evaluate TIL levels in molecular subtypes of breast cancer.