Purpose
To study the characteristics of borderline tumors (BOT) diagnosed during pregnancy, as either first diagnosis or relapse, to evaluate safety of expectant management.
Methods
15 women affected ...by BOT during pregnancy were included, to evaluate clinical and histo-pathological characteristics. Age of patient, parity, gestational age, follow-up time, size of tumor, surgical approach, type and timing of surgery, FIGO stage, and histologic type were obtained through retrospective review.
Results
All patients except one were diagnosed with serous BOT (BOTs). Median follow-up time was 147 ± 57 months. Eight women received first diagnosis of BOT and seven had diagnosis of BOT recurrence during pregnancy, including three with a second relapse and four with a third relapse. BOT were diagnosed at FIGO stage I in most patients (75%) of the first group and in 14.3% of the second group, respectively. Micropapillary pattern was present in 71.4% of patients with first diagnosis of BOT, but only in 14.2% in case of relapse. All relapses were BOTs. No patient with BOT and concomitant pregnancy developed an invasive recurrence later. Overall, 24 relapses occurred in 10 patients (66.7%). Altogether 24 pregnancies occurred during follow-up, with a high livebirth rate (91.6%) and only 2 spontaneous miscarriages.
Conclusion
According to our experience, an “expectation management” could be a safe option in case of both relapse of BOTs during pregnancy and first suspicion of BOT in pregnant women at advanced gestational age.
Abstract
Objectives
To investigate the prevalence of high-risk human papillomavirus (HPV)–negative cervical intraepithelial neoplasia (CIN) and invasive cervical carcinoma (ICC) and to analyze the ...distribution of other genotypes in this subset.
Methods
In total, 431 women who underwent excisional surgical treatment for CIN or ICC at the European Institute of Oncology, Milan, Italy, from January 2016 to December 2017 were retrospectively analyzed. The Linear Array HPV genotyping test (Roche Diagnostics) was performed on a postaliquot from high-risk-HPV–negative liquid-based cervical specimens, when available. Patient characteristics and the prevalence of high-risk-HPV–negative CIN grade 2 or worse (CIN2+) were tabulated. We used t tests to compare age between high-risk-HPV–positive and high-risk-HPV–negative patients.
Results
Overall, 8.9% of CIN2+ and 7.5% of ICC cases were high-risk HPV negative. There was no age difference between high-risk-HPV–negative CIN2+ women (mean SD, 41.3 8.7 years) and high-risk-HPV–positive women (mean SD, 39.5 9.0 years) (P = .28). The Linear Array result was available in 22 cases. Most high-risk-HPV–negative patients were positive for a single other genotype infection (32.6%). HPV 73 was the most prevalent genotype, followed by HPV 53 and HPV 84. HPV 26 was detected in 1 case of ICC.
Conclusions
Our results showed a not-negligible proportion of high-risk-HPV–negative CIN2+, suggesting that cotesting would not miss these cases.
This study aims to analyze the sensitivity of vaginosonography (VGS) and magnetic resonance imaging (MRI) in the preoperative local evaluation of early-stage cervical cancers and to assess their ...accuracy in the detection of tumors, size of the lesions and stromal invasion by comparing them with the final histopathology report. This single-center study included 56 consecutive patients with cervical cancer who underwent VGS and MRI from November 2012 to January 2021. VGS significantly overestimated the lesion size by 2.7 mm (p = 0.002), and MRI underestimated it by 1.9 mm (p = 0.11). Both MRI and VGS had a good concordance with the pathology report (Cohen’s kappa of 0.73 and 0.81, respectively). However, MRI had a false-negative rate (38.1%) that was greater than VGS (0%) in cases of cervical tumor size <2 cm. We found a good concordance between histology and VGS in the stromal infiltration assessment, with 89% sensitivity (95% CI 0.44−0.83) and 89% specificity (95% CI 0.52−0.86). VGS is a simple, inexpensive, widely available, and fast execution method that can complement ultrasound in particular cases and show a good correlation with MRI in the assessment of tumor dimensions, with a better performance in detecting small tumors (<2 cm).
Purpose
Mathematical modeling is a powerful and flexible method to investigate complex phenomena. It discloses the possibility of reproducing expensive as well as invasive experiments in a safe ...environment with limited costs. This makes it suitable to mimic tumor evolution and response to radiotherapy although the reliability of the results remains an issue. Complexity reduction is therefore a critical aspect in order to be able to compare model outcomes to clinical data. Among the factors affecting treatment efficacy, tumor oxygenation is known to play a key role in radiotherapy response. In this work, we aim at relating the oxygenation dynamics, predicted by a macroscale model trained on tumor volumetric data of uterine cervical cancer patients, to vascularization and blood flux indices assessed on Ultrasound Doppler images.
Methods
We propose a macroscale model of tumor evolution based on three dynamics, namely active portion, necrotic portion, and oxygenation. The model parameters were assessed on the volume size of seven cervical cancer patients administered with 28 fractions of intensity modulated radiation therapy (IMRT) (1.8 Gy/fraction). For each patient, five Doppler ultrasound tests were acquired before, during, and after the treatment. The lesion was manually contoured by an expert physician using 4D View® (General Electric Company ‐ Fairfield, Connecticut, United States), which automatically provided the overall tumor volume size along with three vascularization and/or blood flow indices. Volume data only were fed to the model for training purpose, while the predicted oxygenation was compared a posteriori to the measured Doppler indices.
Results
The model was able to fit the tumor volume evolution within 8% error (range: 3–8%). A strong correlation between the intrapatient longitudinal indices from Doppler measurements and oxygen predicted by the model (about 90% or above) was found in three cases. Two patients showed an average correlation value (50–70%) and the remaining two presented poor correlations. The latter patients were the ones featuring the smallest tumor reduction throughout the treatment, typical of hypoxic conditions. Moreover, the average oxygenation value predicted by the model was close to the average vascularization‐flow index (average difference: 7%).
Conclusions
The results suggest that the modeled relation between tumor evolution and oxygen dynamics was reasonable enough to provide realistic oxygenation curves in five cases (correlation greater than 50%) out of seven. In case of nonresponsive tumors, the model failed in predicting the oxygenation trend while succeeded in reproducing the average oxygenation value according to the mean vascularization‐flow index. Despite the need for deeper investigations, the outcomes of the present work support the hypothesis that a simple macroscale model of tumor response to radiotherapy is able to predict the tumor oxygenation. The possibility of an objective and quantitative validation on imaging data discloses the possibility to translate them as decision support tools in clinical practice and to move a step forward in the treatment personalization.
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