Gynecologic tumor board: a radiologist's guide to vulvar and vaginal malignancies.

Primary vulvar and vaginal cancers are rare female genital tract malignancies which are staged using the 2009 International Federation of Gynecology and Obstetrics (FIGO) staging. These cancers account for approximately 2,700 deaths annually in the USA. The most common histologic subtype of both vulvar and vaginal cancers is squamous cell carcinoma, with an increasing role of the human papillomavirus (HPV) in a significant number of these tumors. Lymph node involvement is the hallmark of FIGO stage 3 vulvar cancer while pelvic sidewall involvement is the hallmark of FIGO stage 3 vaginal cancer. Imaging techniques include computed tomography (CT), positron emission tomography (PET)-CT, magnetic resonance imaging (MRI), and PET-MRI. MRI is the imaging modality of choice for preoperative clinical staging of nodal and metastatic involvement while PET-CT is helpful with assessing response to neoadjuvant treatment and for guiding patient management. Determining the pretreatment extent of disease has become more important due to modern tailored operative approaches and use of neoadjuvant chemoradiation therapy to reduce surgical morbidity. Moreover, imaging is used to determine the full extent of disease for radiation planning and for evaluating treatment response. Understanding the relevant anatomy of the vulva and vaginal regions and the associated lymphatic pathways is helpful to recognize the potential routes of spread and to correctly identify the appropriate FIGO stage. The purpose of this article is to review the clinical features, pathology, and current treatment strategies for vulvar and vaginal malignancies and to identify multimodality diagnostic imaging features of these gynecologic cancers, in conjunction with its respective 2009 FIGO staging system guidelines

Gynecologic tumor board: a radiologist’s guide to vulvar and vaginal malignancies

AbstractPrimary vulvar and vaginal cancers are rare female genital tract malignancies which are staged using the 2009 International Federation of Gynecology and Obstetrics (FIGO) staging. These cancers account for approximately 2,700 deaths annually in the USA. The most common histologic subtype of both vulvar and vaginal cancers is squamous cell carcinoma, with an increasing role of the human papillomavirus (HPV) in a significant number of these tumors. Lymph node involvement is the hallmark of FIGO stage 3 vulvar cancer while pelvic sidewall involvement is the hallmark of FIGO stage 3 vaginal cancer. Imaging techniques include computed tomography (CT), positron emission tomography (PET)-CT, magnetic resonance imaging (MRI), and PET-MRI. MRI is the imaging modality of choice for preoperative clinical staging of nodal and metastatic involvement while PET-CT is helpful with assessing response to neoadjuvant treatment and for guiding patient management. Determining the pretreatment extent of disease has become more important due to modern tailored operative approaches and use of neoadjuvant chemoradiation therapy to reduce surgical morbidity. Moreover, imaging is used to determine the full extent of disease for radiation planning and for evaluating treatment response. Understanding the relevant anatomy of the vulva and vaginal regions and the associated lymphatic pathways is helpful to recognize the potential routes of spread and to correctly identify the appropriate FIGO stage. The purpose of this article is to review the clinical features, pathology, and current treatment strategies for vulvar and vaginal malignancies and to identify multimodality diagnostic imaging features of these gynecologic cancers, in conjunction with its respective 2009 FIGO staging system guidelines.</jats:p

A Multitask Approach for Automated Detection and Segmentation of Thyroid Nodules in Ultrasound Images

AbstractAn increase in the incidence and diagnosis of thyroid nodules and thyroid cancer underscores the need for a better approach to nodule detection and risk stratification in ultrasound (US) images that can reduce healthcare costs, patient discomfort, and unnecessary invasive procedures. However, variability in ultrasound technique and interpretation makes the diagnostic process partially subjective. Therefore, an automated approach that detects and segments nodules could improve performance on downstream tasks, such as risk stratification.Current deep learning architectures for segmentation are typically semi-automated because they are evaluated solely on images known to have nodules and do not assess ability to identify suspicious images. However, the proposed multitask approach both detects suspicious images and segments potential nodules; this allows for a clinically translatable model that aptly parallels the workflow for thyroid nodule assessment. The multitask approach is centered on an anomaly detection (AD) module that can be integrated with any U-Net architecture variant to improve image-level nodule detection. Ultrasound studies were acquired from 280 patients at UCLA Health, totaling 9,888 images, and annotated by collaborating radiologists. Of the evaluated models, a multi-scale UNet (MSUNet) with AD achieved the highest F1 score of 0.829 and image-wide Dice similarity coefficient of 0.782 on our hold-out test set. Furthermore, models were evaluated on two external validations datasets to demonstrate generalizability and robustness to data variability. Ultimately, the proposed architecture is an automated multitask method that expands on previous methods by successfully both detecting and segmenting nodules in ultrasound.</jats:p

Human Placenta Blood Flow During Early Gestation With Pseudocontinuous Arterial Spin Labeling MRI

BackgroundNoninvasive measurement of placental blood flow is the major technical challenge for predicting ischemic placenta (IPD). Pseudocontinuous arterial spin labeling (pCASL) MRI was recently shown to be promising, but the potential value in predicting the subsequence development of IPD is not known.PurposeTo derive global and regional placental blood flow parameters from longitudinal measurements of pCASL MRI and to assess the associations between perfusion-related parameters and IPD.Study typeProspective.PopulationEighty-four women completed two pCASL MRI scans (first; 14-18 weeks and second; 19-24 weeks) from prospectively recruited 118 subjects. A total of 69 subjects were included for the analysis, of which 15 subjects developed IPD.Field strength/sequence3T/T2 -weighted half-Fourier single-shot turbo spin-echo (HASTE) and pCASL.AssessmentFour perfusion-related parameters in the placenta were derived: placenta volume, placental blood flow (PBF), high PBF (hPBF), and relative hPBF. The longitudinal changes of the parameters and their association with IPD were tested after being normalizing to the 16th and 20th weeks of gestation.Statistical testsComparisons between two gestational ages within subjects were performed using the paired Wilcoxon tests, and comparisons between normal and IPD groups were performed using the unpaired Wilcoxon tests.ResultsThe difference between the first and second MRI scans was statistically significant for volume (156.6 cm3 vs. 269.7 cm3 , P &lt; 0.001) and PBF (104.9 ml/100g/min vs. 111.3 ml/100g/min, P = 0.02) for normal subjects, indicating an increase in pregnancy with advancing gestation. Of the parameters tested, the difference between the normal and IPD subjects was most pronounced in hPBF (278.1 ml/100g/min vs. 180.7 ml/100g/min, P &lt; 0.001) and relative hPBF (259.1% vs. 183.2%, P &lt; 0.001) at 16 weeks.Data conclusionThe high perfusion-related image parameters for IPD were significantly decreased from normal pregnancy at 14-18 weeks of gestation.Level of evidence2 TECHNICAL EFFICACY STAGE: 1 J. Magn. Reson. Imaging 2020;51:1247-1257

Patient-level thyroid cancer classification using attention multiple instance learning on fused multi-scale ultrasound image features.

For patients with thyroid nodules, the ability to detect and diagnose a malignant nodule is the key to creating an appropriate treatment plan. However, assessments of ultrasound images do not accurately represent malignancy, and often require a biopsy to confirm the diagnosis. Deep learning techniques can classify thyroid nodules from ultrasound images, but current methods depend on manually annotated nodule segmentations. Furthermore, the heterogeneity in the level of magnification across ultrasound images presents a significant obstacle to existing methods. We developed a multi-scale, attention-based multiple-instance learning model which fuses both global and local features of different ultrasound frames to achieve patient-level malignancy classification. Our model demonstrates improved performance with an AUROC of 0.785 (p&lt;0.05) and AUPRC of 0.539, significantly surpassing the baseline model trained on clinical features with an AUROC of 0.667 and AUPRC of 0.444. Improved classification performance better triages the need for biopsy