Pneumomediastinum in late pregnancy: a case report and review of the literature

Background Pneumomediastium is a rare complication of pregnancy or labor. Methods Here, we report our findings in a case report (gravid 5, para 2, gestational age 33 + 4 weeks) and narratively review the current literature on pneumomediastinum in pregnancy or labor. Results Our case is the first case that experienced pneumomediastinum after relatively limited exposure to barotrauma in the current pregnancy. Other reports describe pneumomediastinum after hyperemesis gravidarum or during labor. Treatment is usually conservatively due to the trauma mechanism of barotrauma to the alveoli. Conclusion Physicians should be aware of the possibility of pneumomediastinum in pregnant women with acute thoracic pain in cases of (previous) hyperemesis gravidarum or during labor

Axillary Pathologic Complete Response After Neoadjuvant Systemic Therapy by Breast Cancer Subtype in Patients With Initially Clinically Node-Positive Disease:A Systematic Review and Meta-analysis

This systematic review and meta-analysis pools data from studies in the neoadjuvant setting on axillary pathologic complete response rates for different breast cancer subtypes in patients with initial clinically node-positive disease.Importance An overview of rates of axillary pathologic complete response (pCR) for all breast cancer subtypes, both for patients with and without pathologically proven clinically node-positive disease, is lacking. Objective To provide pooled data of all studies in the neoadjuvant setting on axillary pCR rates for different breast cancer subtypes in patients with initially clinically node-positive disease. Data Sources The electronic databases Embase and PubMed were used to conduct a systematic literature search on July 16, 2020. The references of the included studies were manually checked to identify other eligible studies. Study Selection Studies in the neoadjuvant therapy setting were identified regarding axillary pCR for different breast cancer subtypes in patients with initially clinically node-positive disease (ie, defined as node-positive before the initiation of neoadjuvant systemic therapy). Data Extraction and Synthesis Two reviewers independently selected eligible studies according to the inclusion criteria and extracted all data. All discrepant results were resolved during a consensus meeting. To identify the different subtypes, the subtype definitions as reported by the included articles were used. The random-effects model was used to calculate the overall pooled estimate of axillary pCR for each breast cancer subtype. Main Outcomes and Measures The main outcome of this study was the rate of axillary pCR and residual axillary lymph node disease after neoadjuvant systemic therapy for different breast cancer subtypes, differentiating studies with and without patients with pathologically proven clinically node-positive disease. Results This pooled analysis included 33 unique studies with 57 531 unique patients and showed the following axillary pCR rates for each of the 7 reported subtypes in decreasing order: 60% for hormone receptor (HR)-negative/ERBB2 (formerly HER2)-positive, 59% for ERBB2-positive (HR-negative or HR-positive), 48% for triple-negative, 45% for HR-positive/ERBB2-positive, 35% for luminal B, 18% for HR-positive/ERBB2-negative, and 13% for luminal A breast cancer. No major differences were found in the axillary pCR rates per subtype by analyzing separately the studies of patients with and without pathologically proven clinically node-positive disease before neoadjuvant systemic therapy. Conclusions and Relevance The HR-negative/ERBB2-positive subtype was associated with the highest axillary pCR rate. These data may help estimate axillary treatment response in the neoadjuvant setting and thus select patients for more or less invasive axillary procedures.Question What are the rates of axillary pathologic complete response (pCR) for different breast cancer subtypes in patients with initially clinically node-positive breast cancer? Findings This systematic review and meta-analysis, including 33 unique studies with 57 531 unique patients, showed that the hormone receptor (HR)-negative/ERBB2-positive subtype was associated with the highest axillary pCR rate (60%). The remaining subtypes were associated with the following axillary pCR rates in decreasing order: 59% for ERBB2-positive, 48% for triple-negative, 45% for HR-positive/ERBB2-positive, 35% for luminal B, 18% for HR-positive/ERBB2-negative, and 13% for luminal A breast cancer. Meaning These data can help estimate axillary treatment response in the neoadjuvant setting and thus select patients for more or less invasive axillary procedures

Dedicated Axillary MRI-Based Radiomics Analysis for the Prediction of Axillary Lymph Node Metastasis in Breast Cancer

Simple SummaryThe presence of axillary lymph node metastases in breast cancer patients is an essential factor in axillary surgery and possible additional treatment. This study aimed to investigate the potential of dedicated axillary MRI-based radiomics analysis for the prediction of axillary lymph node metastases. Dedicated axillary MRI examinations provide a very specific and complete field of view of the axilla. Accurate preoperative prediction of axillary lymph node metastases in breast cancer patients using radiomics analysis can aid in clinical decision-making for the type of treatment.Radiomics features may contribute to increased diagnostic performance of MRI in the prediction of axillary lymph node metastasis. The objective of the study was to predict preoperative axillary lymph node metastasis in breast cancer using clinical models and radiomics models based on T2-weighted (T2W) dedicated axillary MRI features with node-by-node analysis. From August 2012 until October 2014, all women who had undergone dedicated axillary 3.0T T2W MRI, followed by axillary surgery, were retrospectively identified, and available clinical data were collected. All axillary lymph nodes were manually delineated on the T2W MR images, and quantitative radiomics features were extracted from the delineated regions. Data were partitioned patient-wise to train 100 models using different splits for the training and validation cohorts to account for multiple lymph nodes per patient and class imbalance. Features were selected in the training cohorts using recursive feature elimination with repeated 5-fold cross-validation, followed by the development of random forest models. The performance of the models was assessed using the area under the curve (AUC). A total of 75 women (median age, 61 years; interquartile range, 51-68 years) with 511 axillary lymph nodes were included. On final pathology, 36 (7%) of the lymph nodes had metastasis. A total of 105 original radiomics features were extracted from the T2W MR images. Each cohort split resulted in a different number of lymph nodes in the training cohorts and a different set of selected features. Performance of the 100 clinical and radiomics models showed a wide range of AUC values between 0.41-0.74 and 0.48-0.89 in the training cohorts, respectively, and between 0.30-0.98 and 0.37-0.99 in the validation cohorts, respectively. With these results, it was not possible to obtain a final prediction model. Clinical characteristics and dedicated axillary MRI-based radiomics with node-by-node analysis did not contribute to the prediction of axillary lymph node metastasis in breast cancer based on data where variations in acquisition and reconstruction parameters were not addressed

Test-Retest Data for the Assessment of Breast MRI Radiomic Feature Repeatability

Background Radiomic features extracted from breast MRI have potential for diagnostic, prognostic, and predictive purposes. However, before they can be used as biomarkers in clinical decision support systems, features need to be repeatable and reproducible. Objective Identify repeatable radiomics features within breast tissue on prospectively collected MRI exams through multiple test-retest measurements. Study Type Prospective. Population 11 healthy female volunteers. Field Strength/Sequence 1.5 T; MRI exams, comprising T2-weighted turbo spin-echo (T2W) sequence, native T1-weighted turbo gradient-echo (T1W) sequence, diffusion-weighted imaging (DWI) sequence using b-values 0/150/800, and corresponding derived ADC maps. Assessment 18 MRI exams (three test-retest settings, repeated on 2 days) per healthy volunteer were examined on an identical scanner using a fixed clinical breast protocol. For each scan, 91 features were extracted from the 3D manually segmented right breast using Pyradiomics, before and after image preprocessing. Image preprocessing consisted of 1) bias field correction (BFC); 2) z-score normalization with and without BFC; 3) grayscale discretization using 32 and 64 bins with and without BFC; and 4) z-score normalization + grayscale discretization using 32 and 64 bins with and without BFC. Statistical Tests Features' repeatability was assessed using concordance correlation coefficient(CCC) for each pair, i.e. each MRI was compared to each of the remaining 17 MRI with a cut-off value of CCC > 0.90. Results Images without preprocessing produced the highest number of repeatable features for both T1W sequence and ADC maps with 15 of 91 (16.5%) and 8 of 91 (8.8%) repeatable features, respectively. Preprocessed images produced between 4 of 91 (4.4%) and 14 of 91 (15.4%), and 6 of 91 (6.6%) and 7 of 91 (7.7%) repeatable features, respectively for T1W and ADC maps. Z-score normalization produced highest number of repeatable features, 26 of 91 (28.6%) in T2W sequences, in these images, no preprocessing produced 11 of 91 (12.1%) repeatable features. Data Conclusion Radiomic features extracted from T1W, T2W sequences and ADC maps from breast MRI exams showed a varying number of repeatable features, depending on the sequence. Effects of different preprocessing procedures on repeatability of features were different for each sequence. Level of Evidence 2 Technical Efficacy Stage