Acute pelvic pain is a common presenting complaint in both the emergency room and outpatient settings. Pelvic pain of gynecologic origin in postmenopausal women occurs less frequently than in premenopausal women; however, it has important differences in etiology. The most common causes of postmenopausal pelvic pain from gynecologic origin are ovarian cysts, uterine fibroids, pelvic inflammatory disease, and ovarian neoplasm. Other etiologies of pelvic pain are attributable to urinary, gastrointestinal, and vascular systems. As the optimal imaging modality varies for these etiologies, it is important to narrow the differential diagnosis before choosing the initial diagnostic imaging examination. Transabdominal and transvaginal ultrasound are the best initial imaging techniques when the differential is primarily of gynecologic origin. CT with intravenous (IV) contrast is more useful if the differential diagnosis remains broad. MRI without IV contrast or MRI without and with IV contrast, as well as CT without IV contrast may also be used for certain differential considerations. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.
Publications
2021
Neoadjuvant therapy is increasingly being used to treat early-stage triple-negative and human epidermal growth factor 2-overexpressing breast cancers, as well as locally advanced and inflammatory breast cancers. The rationales for neoadjuvant therapy are to shrink tumor size and potentially decrease the extent of surgery, to serve as an in vivo test of response to therapy, and to reveal prognostic information for the patient. MRI is the most accurate modality to demonstrate response to therapy and to help ensure accurate presurgical planning. Changes in lesion diameter, volume, and enhancement are used to predict complete response, partial response, or nonresponse to therapy. However, residual disease may be overestimated or underestimated at MRI. Fibrosis, necrotic tumors, and residual benign masses may be causes of overestimation of residual disease. Nonmass lesions, invasive lobular carcinoma, hormone receptor-positive tumors, nonconcentric shrinkage patterns, the use of antiangiogenic therapy, and late-enhancing foci may be causes of underestimation of residual disease. In patients with known axillary lymph node metastasis, neoadjuvant therapy may be followed by targeted axillary dissection to avoid the potential morbidity associated with an axillary lymph node dissection. Diffusion-weighted imaging, radiomics, machine learning, and deep learning methods are under investigation to improve MRI accuracy in predicting treatment response.©RSNA, 2021.
BACKGROUND: No large dataset-derived standard has been established for normal or pathologic human cerebral ventricular and cranial vault volumes. Automated volumetric measurements could be used to assist in diagnosis and follow-up of hydrocephalus or craniofacial syndromes. In this work, we use deep learning algorithms to measure ventricular and cranial vault volumes in a large dataset of head computed tomography (CT) scans.
METHODS: A cross-sectional dataset comprising 13,851 CT scans was used to deploy U-Net deep learning networks to segment and quantify lateral cerebral ventricular and cranial vault volumes in relation to age and sex. The models were validated against manual segmentations. Corresponding radiologic reports were annotated using a rule-based natural language processing framework to identify normal scans, cerebral atrophy, or hydrocephalus.
RESULTS: U-Net models had high fidelity to manual segmentations for lateral ventricular and cranial vault volume measurements (Dice index, 0.878 and 0.983, respectively). The natural language processing identified 6239 (44.7%) normal radiologic reports, 1827 (13.1%) with cerebral atrophy, and 1185 (8.5%) with hydrocephalus. Age-based and sex-based reference tables with medians, 25th and 75th percentiles for scans classified as normal, atrophy, and hydrocephalus were constructed. The median lateral ventricular volume in normal scans was significantly smaller compared with hydrocephalus (15.7 vs. 82.0 mL; P < 0.001).
CONCLUSIONS: This is the first study to measure lateral ventricular and cranial vault volumes in a large dataset, made possible with artificial intelligence. We provide a robust method to establish normal values for these volumes and a tool to report these on CT scans when evaluating for hydrocephalus.
PURPOSE: To assess if a templated algorithm can improve the diagnostic performance of MRI for characterization of T2 isointense and hypointense renal masses.
METHODS: In this retrospective study, 60 renal masses with histopathologic diagnoses that were also confirmed as T2 iso- or hypointense on MRI were identified (mean ± standard deviation, range: 3.9 ± 2.5, 1.0-13.7 cm). Two semi-quantitative diagnostic algorithms were created based on MRI features of renal masses reported in the literature. Three body-MRI trained radiologists provided clinical diagnoses based on their experience and separately provided semiquantitative data for each components of the two algorithms. The algorithms were applied separately by a radiology trainee without additional interpretive input. Logistic regression was used to compare the accuracy of the three methods in distinguishing malignant versus benign lesions and in diagnosing the exact histopathology. Inter-reader agreement for each method was calculated using Fleiss' kappa statistics.
RESULTS: The accuracy of the two algorithms and clinical experience were similar (70%, 69%, and 64%, respectively, p = 0.22-0.32), with fair to moderate inter-reader agreement (Fleiss's kappa: r = 0.375, r = 0.308, r = 0.375, respectively, all p < 0.0001). The accuracy of the two algorithms and clinical experience in diagnosing specific histopathology were also no different from each other (34%, 29%, and 32%, respectively, p = 0.49-0.74), with fair to moderate inter-reader agreement (Fleiss's kappa: r = 0.20, r = 0.28, r = 0.375, respectively, all p < 0.0001).
CONCLUSION: Semi-quantitative templated algorithms based on MRI features of renal masses did not improve the ability to diagnose T2 iso- and hypointense renal masses when compared to unassisted interpretation by body MR trained subspecialists.
Reflector-guided localization uses a nonradioactive radar implant for wire-free presurgical breast lesion localization. A single-institution retrospective evaluation found lower rates of positive margins and of close margins for reflector-guided localizations compared with wire localizations, resulting in a statistically significant decrease in the re-excision rates (p = 0.015). The two approaches did not show statistically significant difference in localization time and OR time. Technical challenges included particulars inherent in reflector placement, while patient factors included special considerations for reflector placement in the postsurgical breast. Despite novel challenges, we found reflector-guided localization to be accurate and efficient.
OBJECTIVE. Diagnostic accuracy of core needle biopsy (CNB) for adipocytic tumors can be low because of sampling error from these often large, heterogeneous lesions. The purpose of this study was to evaluate the diagnostic accuracy of image-guided CNB for various adipocytic tumors in comparison with excisional pathology. MATERIALS AND METHODS. Adipocytic tumors (n = 77) of all adult patients undergoing image-guided CNB and subsequent surgical excision of an adipocytic tumor at a tertiary referral center between 2005 and 2019 were studied. To determine concordance, we compared pathologic diagnoses based on CNB to the reference standard of pathologic diagnoses after surgical excision. Tumors were divided into three categories (benign lipomatous tumors [lipoma, lipoma variants, hibernomas], atypical lipomatous tumors [ALTs] or well-differentiated liposarcomas [WDLs], and higher grade liposarcomas [myxoid, dedifferentiated, pleomorphic]), and diagnostic accuracy was calculated for each category. RESULTS. In 73 of 77 adipocytic tumors (95%), diagnosis at CNB and diagnosis after excision were concordant. Accuracy of diagnosis was poorer for ALTs and WDLs than for the other two categories, and the difference was statistically significant (p < .002). For the 29 benign lipomatous tumors and the 27 higher-grade liposarcomas, diagnoses at CNB and after excision were concordant in all cases (100%). Seventeen of the 21 tumors (81%) diagnosed as ALTs or WDLs at CNB had a concordant diagnosis after excision; four of the 21 were upgraded (dedifferentiated liposarcoma, n = 3; myxoid liposarcoma, n = 1). CONCLUSION. CNB provides high diagnostic accuracy for adipocytic tumors, particularly for benign lipomatous tumors and higher grade liposarcomas. However, though still high at 81%, diagnostic accuracy of CNB is not as high for tumors diagnosed as ALTs or WDLs. Awareness of this limitation is important when determining management, particularly of cases of ALT or WDL for which surgery is not planned.
Lack of diversity in Radiology is a public health problem and may be self perpetuating as diverse candidates view the field as hostile to their entry and advancement, and consequently do not apply into the field. Solutions require understanding the obstacles, which range from enrollment in medical school to achieving leadership positions in Radiology. An understanding of the effect of demographic data on diversity in Radiology, disparate effects of Step examinations, medical school grades and induction into academic honor societies, and existing faculty disparities will allow us to better recruit, train, and retain a diverse group of physicians in our field. The downstream effect of a diverse workforce is improvement in health outcomes and disparities in medical care for our communities.
PURPOSE: Hepatic thermal ablation therapy can result in c-Met-mediated off-target stimulation of distal tumor growth. The purpose of this study was to determine if a similar effect on tumor metabolism could be detected in vivo with hyperpolarized 13C MRI.
MATERIALS AND METHODS: In this prospective study, female Fisher rats (n = 28, 120-150 g) were implanted with R3230 rat breast adenocarcinoma cells and assigned to either: sham surgery, hepatic radiofrequency ablation (RFA), or hepatic RFA + adjuvant c-Met inhibition with PHA-665752 (RFA + PHA). PHA-665752 was administered at 0.83 mg/kg at 24 h post-RFA. Tumor growth was measured daily. MRI was performed 24 h before and 72 h after treatment on 14 rats, and the conversion of 13C-pyruvate into 13C-lactate within each tumor was quantified as lactate:pyruvate ratio (LPR). Comparisons of tumor growth and LPR were performed using paired and unpaired t-tests.
RESULTS: Hepatic RFA alone resulted in increased growth of the distant tumor compared to sham treatment (0.50 ± 0.13 mm/day versus 0.11 ± 0.07 mm/day; p < 0.001), whereas RFA + PHA (0.06 ± 0.13 mm/day) resulted in no significant change from sham treatment (p = 0.28). A significant increase in LPR was seen following hepatic RFA (+0.016 ± 0.010, p = 0.02), while LPR was unchanged for sham treatment (-0.048 ± 0.051, p = 0.10) or RFA + PHA (0.003 ± 0.041, p = 0.90).
CONCLUSION: In vivo hyperpolarized 13C MRI can detect hepatic RFA-induced increase in lactate flux within a distant R3230 tumor, which correlates with increased tumor growth. Adjuvant inhibition of c-Met suppresses these off-target effects, supporting a role for the HGF/c-Met signaling axis in these tumorigenic responses.