Small Cell Carcinoma of the Vagina: First Systematic Review of Case Reports and Proposal of a Management Algorithm.

OBJECTIVES: Small cell carcinoma of the vagina (SmCCV) is an extremely rare disease. Evidence-based data and specific guidelines are lacking. We conducted the first systematic review of case reports to provide the most overall picture of SmCCV. MATERIALS AND METHODS: Literature search in PubMed and Scopus was performed using the terms "small cell carcinoma" and "vagina." English-language case reports of primary SmCCV up to January 2022 were included. RESULTS: Twenty-nine articles describing 44 cases met our inclusion criteria. We report a new case of our hospital. The global median overall survival (mOS) was 12.00 months (95% CI = 9.31-14.69). The mOS was not reached for stage I, and it was 12.00, 12.00, 9.00, and 8.00 months for stages II, III, IVA, and IVB, respectively (statistically significant differences between stage I and stages II, III, or IVA [log rank p = .003-.017]). Thirty-five cases received local treatments (77.8%). The mOS of patients treated with surgery ± complementary chemotherapy, radiotherapy ± complementary chemotherapy, chemoradiation ± complementary chemotherapy, and surgery + radiotherapy ± complementary chemotherapy were 11.00, 12.00, 17.00, and 29.00 months, respectively. The use of adjuvant or neoadjuvant chemotherapy (64.5%, mostly platinum + etoposide) showed longer mOS (77.00 vs 15.00 months). Four of 5 tested cases presented human papillomavirus infection, 3 of them presenting type 18. CONCLUSIONS: Small cell carcinoma of the vagina shows dismal prognosis. Multimodal local management plus complementary chemotherapy seems to achieve better outcomes. Human papillomavirus could be related to the development of SmCCV. A diagnostic-therapeutic algorithm is proposed.

Is oligoprogression a potentially curable disease in epidermal growth factor receptor mutant lung adenocarcinoma?

Third-generation epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) have shown impressive results in EGFR mutant lung cancer (LC) patients in terms of disease control rate with a positive impact on overall survival. Nevertheless, after months of treatment with targeted therapy, progression inevitably occurs. Some patients develop oligoprogression and local treatment is required for optimal disease control while maintaining EGFR-TKIs. This work features a clinical case of a patient harboring an EGFR mutant LC undergoing oligoprogression to EGFR-TKIs, first into the brain and afterward to the primary tumor, requiring local ablative strategies, including primary tumor resection three years after the start of osimertinib. Currently, the patient is still alive and continues with a complete response upon EGFR-TKIs maintenance. Hence, oligoprogression, even in driven oncogenic tumors, represents a distinct biological entity and potential curative disease that deserves particular consideration in multidisciplinary tumor boards. In this case, tumor primary resection after three years of the initial diagnosis represents a paradigm shift in the treatment of EGFR mutant patients.

A novel intelligent radiomic analysis of perfusion SPECT/CT images to optimize pulmonary embolism diagnosis in COVID-19 patients.

BACKGROUND: COVID-19 infection, especially in cases with pneumonia, is associated with a high rate of pulmonary embolism (PE). In patients with contraindications for CT pulmonary angiography (CTPA) or non-diagnostic CTPA, perfusion single-photon emission computed tomography/computed tomography (Q-SPECT/CT) is a diagnostic alternative. The goal of this study is to develop a radiomic diagnostic system to detect PE based only on the analysis of Q-SPECT/CT scans. METHODS: This radiomic diagnostic system is based on a local analysis of Q-SPECT/CT volumes that includes both CT and Q-SPECT values for each volume point. We present a combined approach that uses radiomic features extracted from each scan as input into a fully connected classification neural network that optimizes a weighted cross-entropy loss trained to discriminate between three different types of image patterns (pixel sample level): healthy lungs (control group), PE and pneumonia. Four types of models using different configuration of parameters were tested. RESULTS: The proposed radiomic diagnostic system was trained on 20 patients (4,927 sets of samples of three types of image patterns) and validated in a group of 39 patients (4,410 sets of samples of three types of image patterns). In the training group, COVID-19 infection corresponded to 45% of the cases and 51.28% in the test group. In the test group, the best model for determining different types of image patterns with PE presented a sensitivity, specificity, positive predictive value and negative predictive value of 75.1%, 98.2%, 88.9% and 95.4%, respectively. The best model for detecting pneumonia presented a sensitivity, specificity, positive predictive value and negative predictive value of 94.1%, 93.6%, 85.2% and 97.6%, respectively. The area under the curve (AUC) was 0.92 for PE and 0.91 for pneumonia. When the results obtained at the pixel sample level are aggregated into regions of interest, the sensitivity of the PE increases to 85%, and all metrics improve for pneumonia. CONCLUSION: This radiomic diagnostic system was able to identify the different lung imaging patterns and is a first step toward a comprehensive intelligent radiomic system to optimize the diagnosis of PE by Q-SPECT/CT. HIGHLIGHTS: Artificial intelligence applied to Q-SPECT/CT is a diagnostic option in patients with contraindications to CTPA or a non-diagnostic test in times of COVID-19.

Automatic construction of 3D-ASM intensity models by simulating image acquisition: application to myocardial gated SPECT studies.

Active shape models bear a great promise for model-based medical image analysis. Their practical use, though, is undermined due to the need to train such models on large image databases. Automatic building of point distribution models (PDMs) has been successfully addressed and a number of autolandmarking techniques are currently available. However, the need for strategies to automatically build intensity models around each landmark has been largely overlooked in the literature. This work demonstrates the potential of creating intensity models automatically by simulating image generation. We show that it is possible to reuse a 3D PDM built from computed tomography (CT) to segment gated single photon emission computed tomography (gSPECT) studies. Training is performed on a realistic virtual population where image acquisition and formation have been modeled using the SIMIND Monte Carlo simulator and ASPIRE image reconstruction software, respectively. The dataset comprised 208 digital phantoms (4D-NCAT) and 20 clinical studies. The evaluation is accomplished by comparing point-to-surface and volume errors against a proper gold standard. Results show that gSPECT studies can be successfully segmented by models trained under this scheme with subvoxel accuracy. The accuracy in estimated LV function parameters, such as end diastolic volume, end systolic volume, and ejection fraction, ranged from 90.0% to 94.5% for the virtual population and from 87.0% to 89.5% for the clinical population.