Diagnostic Accuracy of CT for Metastatic Epidural Spinal Cord Compression.

BACKGROUND: Early diagnosis of metastatic epidural spinal cord compression (MESCC) is vital to expedite therapy and prevent paralysis. Staging CT is performed routinely in cancer patients and presents an opportunity for earlier diagnosis. METHODS: This retrospective study included 123 CT scans from 101 patients who underwent spine MRI within 30 days, excluding 549 CT scans from 216 patients due to CT performed post-MRI, non-contrast CT, or a gap greater than 30 days between modalities. Reference standard MESCC gradings on CT were provided in consensus via two spine radiologists (11 and 7 years of experience) analyzing the MRI scans. CT scans were labeled using the original reports and by three radiologists (3, 13, and 14 years of experience) using dedicated CT windowing. RESULTS: For normal/none versus low/high-grade MESCC per CT scan, all radiologists demonstrated almost perfect agreement with kappa values ranging from 0.866 (95% CI 0.787-0.945) to 0.947 (95% CI 0.899-0.995), compared to slight agreement for the reports (kappa = 0.095, 95%CI -0.098-0.287). Radiologists also showed high sensitivities ranging from 91.51 (95% CI 84.49-96.04) to 98.11 (95% CI 93.35-99.77), compared to 44.34 (95% CI 34.69-54.31) for the reports. CONCLUSION: Dedicated radiologist review for MESCC on CT showed high interobserver agreement and sensitivity compared to the current standard of care.

Impact of contrast-enhanced mammography in surgical management of breast cancers for women with dense breasts: a dual-center, multi-disciplinary study in Asia.

OBJECTIVE: To evaluate the impact of pre-operative contrast-enhanced mammography (CEM) in breast cancer patients with dense breasts. METHODS: We conducted a retrospective review of 232 histologically proven breast cancers in 200 women (mean age: 53.4 years ± 10.2) who underwent pre-surgical CEM imaging across two Asian institutions (Singapore and Taiwan). Majority (95.5%) of patients had dense breast tissue (BI-RADS category C or D). Surgical decision was recorded in a simulated blinded multi-disciplinary team setting on two separate scenarios: (i) pre-CEM setting with standard imaging, and clinical and histopathological results; and (ii) post-CEM setting with new imaging and corresponding histological findings from CEM. Alterations in surgical plan (if any) because of CEM imaging were recorded. Predictors CEM of patients who benefitted from surgical plan alterations were evaluated using logistic regression. RESULTS: CEM resulted in altered surgical plans in 36 (18%) of 200 patients in this study. CEM discovered clinically significant larger tumor size or extent in 24 (12%) patients and additional tumors in 12 (6%) patients. CEM also detected additional benign/false-positive lesions in 13 (6.5%) of the 200 patients. Significant predictors of patients who benefitted from surgical alterations found on multivariate analysis were pre-CEM surgical decision for upfront breast conservation (OR, 7.7; 95% CI, 1.9-32.1; p = 0.005), architectural distortion on mammograms (OR, 7.6; 95% CI, 1.3-42.9; p = .022), and tumor size of ≥ 1.5 cm (OR, 1.5; 95% CI, 1.0-2.2; p = .034). CONCLUSION: CEM is an effective imaging technique for pre-surgical planning for Asian breast cancer patients with dense breasts. KEY POINTS: • CEM significantly altered surgical plans in 18% (nearly 1 in 5) of this Asian study cohort with dense breasts. • Significant patient and imaging predictors for surgical plan alteration include (i) patients considered for upfront breast-conserving surgery; (ii) architectural distortion lesions; and (iii) tumor size of ≥ 1.5 cm. • Additional false-positive/benign lesions detected through CEM were uncommon, affecting only 6.5% of the study cohort.

Improved Productivity Using Deep Learning-assisted Reporting for Lumbar Spine MRI.

Background Lumbar spine MRI studies are widely used for back pain assessment. Interpretation involves grading lumbar spinal stenosis, which is repetitive and time consuming. Deep learning (DL) could provide faster and more consistent interpretation. Purpose To assess the speed and interobserver agreement of radiologists for reporting lumbar spinal stenosis with and without DL assistance. Materials and Methods In this retrospective study, a DL model designed to assist radiologists in the interpretation of spinal canal, lateral recess, and neural foraminal stenoses on lumbar spine MRI scans was used. Randomly selected lumbar spine MRI studies obtained in patients with back pain who were 18 years and older over a 3-year period, from September 2015 to September 2018, were included in an internal test data set. Studies with instrumentation and scoliosis were excluded. Eight radiologists, each with 2-13 years of experience in spine MRI interpretation, reviewed studies with and without DL model assistance with a 1-month washout period. Time to diagnosis (in seconds) and interobserver agreement (using Gwet κ) were assessed for stenosis grading for each radiologist with and without the DL model and compared with test data set labels provided by an external musculoskeletal radiologist (with 32 years of experience) as the reference standard. Results Overall, 444 images in 25 patients (mean age, 51 years ± 20 [SD]; 14 women) were evaluated in a test data set. DL-assisted radiologists had a reduced interpretation time per spine MRI study, from a mean of 124-274 seconds (SD, 25-88 seconds) to 47-71 seconds (SD, 24-29 seconds) (P < .001). DL-assisted radiologists had either superior or equivalent interobserver agreement for all stenosis gradings compared with unassisted radiologists. DL-assisted general and in-training radiologists improved their interobserver agreement for four-class neural foraminal stenosis, with κ values of 0.71 and 0.70 (with DL) versus 0.39 and 0.39 (without DL), respectively (both P < .001). Conclusion Radiologists who were assisted by deep learning for interpretation of lumbar spinal stenosis on MRI scans showed a marked reduction in reporting time and superior or equivalent interobserver agreement for all stenosis gradings compared with radiologists who were unassisted by deep learning. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Hayashi in this issue.

Effect of Training Data Volume on Performance of Convolutional Neural Network Pneumothorax Classifiers.

Large datasets with high-quality labels required to train deep neural networks are challenging to obtain in the radiology domain. This work investigates the effect of training dataset size on the performance of deep learning classifiers, focusing on chest radiograph pneumothorax detection as a proxy visual task in the radiology domain. Two open-source datasets (ChestX-ray14 and CheXpert) comprising 291,454 images were merged and convolutional neural networks trained with stepwise increase in training dataset sizes. Model iterations at each dataset volume were evaluated on an external test set of 525 emergency department chest radiographs. Learning curve analysis was performed to fit the observed AUCs for all models generated. For all three network architectures tested, model AUCs and accuracy increased rapidly from 2 × 103 to 20 × 103 training samples, with more gradual increase until the maximum training dataset size of 291 × 103 images. AUCs for models trained with the maximum tested dataset size of 291 × 103 images were significantly higher than models trained with 20 × 103 images: ResNet-50: AUC20k = 0.86, AUC291k = 0.95, p < 0.001; DenseNet-121 AUC20k = 0.85, AUC291k = 0.93, p < 0.001; EfficientNet AUC20k = 0.92, AUC 291 k = 0.98, p < 0.001. Our study established learning curves describing the relationship between dataset training size and model performance of deep learning convolutional neural networks applied to a typical radiology binary classification task. These curves suggest a point of diminishing performance returns for increasing training data volumes, which algorithm developers should consider given the high costs of obtaining and labelling radiology data.

Detection of Pneumothorax with Deep Learning Models: Learning From Radiologist Labels vs Natural Language Processing Model Generated Labels.

RATIONALE AND OBJECTIVES: To compare the performance of pneumothorax deep learning detection models trained with radiologist versus natural language processing (NLP) labels on the NIH ChestX-ray14 dataset. MATERIALS AND METHODS: The ChestX-ray14 dataset consisted of 112,120 frontal chest radiographs with 5302 positive and 106, 818 negative labels for pneumothorax using NLP (dataset A). All 112,120 radiographs were also inspected by 4 radiologists leaving a visually confirmed set of 5,138 positive and 104,751 negative for pneumothorax (dataset B). Datasets A and B were used independently to train 3 convolutional neural network (CNN) architectures (ResNet-50, DenseNet-121 and EfficientNetB3). All models' area under the receiver operating characteristic curve (AUC) were evaluated with the official NIH test set and an external test set of 525 chest radiographs from our emergency department. RESULTS: There were significantly higher AUCs on the NIH internal test set for CNN models trained with radiologist vs NLP labels across all architectures. AUCs for the NLP/radiologist-label models were 0.838 (95%CI:0.830, 0.846)/0.881 (95%CI:0.873,0.887) for ResNet-50 (p = 0.034), 0.839 (95%CI:0.831,0.847)/0.880 (95%CI:0.873,0.887) for DenseNet-121, and 0.869 (95%CI: 0.863,0.876)/0.943 (95%CI: 0.939,0.946) for EfficientNetB3 (p ≤0.001). Evaluation with the external test set also showed higher AUCs (p <0.001) for the CNN models trained with radiologist versus NLP labels across all architectures. The AUCs for the NLP/radiologist-label models were 0.686 (95%CI:0.632,0.740)/0.806 (95%CI:0.758,0.854) for ResNet-50, 0.736 (95%CI:0.686, 0.787)/0.871 (95%CI:0.830,0.912) for DenseNet-121, and 0.822 (95%CI: 0.775,0.868)/0.915 (95%CI: 0.882,0.948) for EfficientNetB3. CONCLUSION: We demonstrated improved performance and generalizability of pneumothorax detection deep learning models trained with radiologist labels compared to models trained with NLP labels.

Infiltrative pattern of metastatic invasive lobular breast carcinoma in the abdomen: a pictorial review.

Invasive lobular carcinoma (ILC) has a greater tendency to metastasize to the peritoneum, retroperitoneum, and gastrointestinal (GI) tract as compared to invasive carcinoma of no special type (NST). Like primary ILC in the breast, ILC metastases are frequently infiltrative and hypometabolic, rather than mass forming and hypermetabolic in nature. This renders them difficult to detect on conventional and metabolic imaging studies. As a result, intra-abdominal ILC metastases are often detected late, with patients presenting with clinical complications such as liver failure, hydronephrosis, or bowel obstruction. In patients with known history of ILC, certain imaging features are very suggestive of infiltrative metastatic ILC. These include retroperitoneal or peritoneal nodularity and linitis plastica appearance of the bowel. Recognition of linitis plastica on imaging should prompt deep or repeat biopsies. In this pictorial review, the authors aim to familiarize readers with imaging features and pitfalls for evaluation of intra-abdominal metastatic ILC. Awareness of these will allow the radiologist to assess these patients with a high index of suspicion and aid detection of metastatic disease. Also, this can direct histopathology and immunohistochemical staining to obtain the correct diagnosis in suspected metastatic disease.

Deep Learning Systems for Pneumothorax Detection on Chest Radiographs: A Multicenter External Validation Study.

PURPOSE: To assess the generalizability of a deep learning pneumothorax detection model on datasets from multiple external institutions and examine patient and acquisition factors that might influence performance. MATERIALS AND METHODS: In this retrospective study, a deep learning model was trained for pneumothorax detection by merging two large open-source chest radiograph datasets: ChestX-ray14 and CheXpert. It was then tested on six external datasets from multiple independent institutions (labeled A-F) in a retrospective case-control design (data acquired between 2016 and 2019 from institutions A-E; institution F consisted of data from the MIMIC-CXR dataset). Performance on each dataset was evaluated by using area under the receiver operating characteristic curve (AUC) analysis, sensitivity, specificity, and positive and negative predictive values, with two radiologists in consensus being used as the reference standard. Patient and acquisition factors that influenced performance were analyzed. RESULTS: The AUCs for pneumothorax detection for external institutions A-F were 0.91 (95% CI: 0.88, 0.94), 0.97 (95% CI: 0.94, 0.99), 0.91 (95% CI: 0.85, 0.97), 0.98 (95% CI: 0.96, 1.0), 0.97 (95% CI: 0.95, 0.99), and 0.92 (95% CI: 0.90, 0.95), respectively, compared with the internal test AUC of 0.93 (95% CI: 0.92, 0.93). The model had lower performance for small compared with large pneumothoraces (AUC, 0.88 [95% CI: 0.85, 0.91] vs AUC, 0.96 [95% CI: 0.95, 0.97]; P = .005). Model performance was not different when a chest tube was present or absent on the radiographs (AUC, 0.95 [95% CI: 0.92, 0.97] vs AUC, 0.94 [95% CI: 0.92, 0.05]; P > .99). CONCLUSION: A deep learning model trained with a large volume of data on the task of pneumothorax detection was able to generalize well to multiple external datasets with patient demographics and technical parameters independent of the training data.Keywords: Thorax, Computer Applications-Detection/DiagnosisSee also commentary by Jacobson and Krupinski in this issue.Supplemental material is available for this article.©RSNA, 2021.

Development of a microRNA Panel for Classification of Abnormal Mammograms for Breast Cancer.

Mammography is extensively used for breast cancer screening but has high false-positive rates. Here, prospectively collected blood samples were used to identify circulating microRNA (miRNA) biomarkers to discriminate between malignant and benign breast lesions among women with abnormal mammograms. The Discovery cohort comprised 72 patients with breast cancer and 197 patients with benign breast lesions, while the Validation cohort had 73 and 196 cancer and benign cases, respectively. Absolute expression levels of 324 miRNAs were determined using RT-qPCR. miRNA biomarker panels were identified by: (1) determining differential expression between malignant and benign breast lesions, (2) focusing on top differentially expressed miRNAs, and (3) building panels from an unbiased search among all expressed miRNAs. Two-fold cross-validation incorporating a feature selection algorithm and logistic regression was performed. A six-miRNA biomarker panel identified by the third strategy, had an area under the curve (AUC) of 0.785 and 0.774 in the Discovery and Validation cohorts, respectively, and an AUC of 0.881 when differentiating between cases versus those with benign lesions or healthy individuals with normal mammograms. Biomarker panel scores increased with tumor size, stage and number of lymph nodes involved. Our work demonstrates that circulating miRNA signatures can potentially be used with mammography to differentiate between patients with malignant and benign breast lesions.

Correct determination of the enhancement curve is critical to ensure accurate diagnosis using the Kaiser score as a clinical decision rule for breast MRI.

OBJECTIVES: the Kaiser score is increasingly recognized as a valuable tool to improve breast MRI interpretation. Contrast enhancement kinetics are the second most important diagnostic criterion, thus defining the curve type plays a crucial role in Kaiser score assessment. We investigate whether the timepoint used to determine the initial enhancement (earlyor peak) for the signal-intensity time curve analysis affects the diagnostic performance of the Kaiser score. METHODS: This IRB-approved, retrospective, single-center study included 70 consecutives histologically verified breast MRI cases. Two off-site breast radiologists independently read all examinations using the Kaiser score, assessing the initial enhancement using three approaches: -first (1 st), second (2nd) and peak (maximum) of either 1 st or 2nd post-contrast timepoints. The initial enhancement was then compared to the last timepoint (delayed enhancement) to determine the curve type. Visual assessment of curve types was used for this study. Diagnostic performance was evaluated by receiver operating characteristics (ROC) analysis. RESULTS: Kaiser score reading results using the peak enhancement of either the first or second timepoint performed significantly better than the other approaches (P < 0.05, respectively) and specifically achieved higher sensitivity. Diagnostic accuracy (AUC area under the curve) ranged between 85.4 % and 91.6 %, without significant differences between the two readers (P < 0.5). CONCLUSIONS: Diagnostic performance of the Kaiser score is significantly influenced by how the initial enhancement timepoint is determined. Peak enhancement should be used as initial timepoint to avoid pitfalls due to timing or physiological differences.

External Validation of a Risk Stratification Score for B3 Breast Lesions Detected at Ultrasound Core Needle Biopsy.

OBJECTIVE: The aim of this study was to externally validate the feasibility and robustness of a risk-stratification score for B3 lesions based on clinical, pathological, and radiological data for improved clinical decision making. METHODS: 129 consecutive histologically confirmed B3 lesions diagnosed at ultrasound-guided biopsy at our institution were included in this retrospective study. Patient- and lesion-related variables were independently assessed by two blinded breast radiologists (R1, R2), by assigning each feature a score from 0 to 2 (maximum sum-score of 5). Sensitivity, specificity, positive and negative predictive values were calculated at two different thresholds (≥1 and 2). Categorical variables were compared using Chi-squared and Fisher exact tests. The diagnostic accuracy of the score to distinguish benign from malignant B3 lesions was assessed by receiver operating characteristic (ROC) analysis. RESULTS: Surgery was performed on 117/129 (90.6%) lesions and 11 of these 117 (9.4%) lesions were malignant. No cancers were found at follow-up of at least 24 months. Area under the ROC-curve was 0.736 (R1) to 0.747 (R2), with no significant difference between the two readers (p = 0.5015). Using a threshold of ≥1, a sensitivity, specificity, PPV, and NPV of 90%/90% (R1/R2), 39%/38% (R1/R2), 11%/12% (R1/R2) and 97%/98% (R1/R2) were identified. Both readers classified 47 lesions with a score ≤1 (low risk of associated malignancy). Of these, only one malignant lesion was underdiagnosed (Ductal carcinoma in situ-G1). CONCLUSIONS: In our external validation, the score showed a high negative predictive value and has the potential to reduce unnecessary surgeries or re-biopsies for ultrasound-detected B3-lesions by up to 39%.

Ultrasound Guided Optoacoustic Tomography in Assessment of Tumor Margins for Lumpectomies.

PURPOSE: To determine the accuracy of a handheld ultrasound-guided optoacoustic tomography (US-OT) probe developed for human deep-tissue imaging in ex vivo assessment of tumor margins postlumpectomy. METHODS: A custom-built two-dimensional (2D) US-OT-handheld probe was used to scan 15 lumpectomy breast specimens. Optoacoustic signals acquired at multiple wavelengths between 700 and 1100 nm were reconstructed using model linear algorithm, followed by spectral unmixing for lipid and deoxyhemoglobin (Hb). Distribution maps of lipid and Hb on the anterior, posterior, superior, inferior, medial, and lateral margins of the specimens were inspected for margin involvement, and results were correlated with histopathologic findings. The agreement in tumor margin assessment between US-OT and histopathology was determined using the Bland-Altman plot. Accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of margin assessment using US-OT were calculated. RESULTS: Ninety margins (6 × 15 specimens) were assessed. The US-OT probe resolved blood vessels and lipid up to a depth of 6 mm. Negative and positive margins were discriminated by marked differences in the distribution patterns of lipid and Hb. US-OT assessments were concordant with histopathologic findings in 87 of 89 margins assessed (one margin was uninterpretable and excluded), with diagnostic accuracy of 97.9% (kappa = 0.79). The sensitivity, specificity, PPV, and NPV were 100% (4/4), 97.6% (83/85), 66.7% (4/6), and 100% (83/83), respectively. CONCLUSION: US-OT was capable of providing distribution maps of lipid and Hb in lumpectomy specimens that predicted tumor margins with high sensitivity and specificity, making it a potential tool for intraoperative tumor margin assessment.

Convolutional Neural Networks for Automated Fracture Detection and Localization on Wrist Radiographs.

PURPOSE: To demonstrate the feasibility and performance of an object detection convolutional neural network (CNN) for fracture detection and localization on wrist radiographs. MATERIALS AND METHODS: Institutional review board approval was obtained with waiver of consent for this retrospective study. A total of 7356 wrist radiographic studies were extracted from a hospital picture archiving and communication system. Radiologists annotated all radius and ulna fractures with bounding boxes. The dataset was split into training (90%) and validation (10%) sets and used to train fracture localization models for frontal and lateral images. Inception-ResNet Faster R-CNN architecture was implemented as a deep learning model. The models were tested on an unseen test set of 524 consecutive emergency department wrist radiographic studies with two radiologists in consensus as the reference standard. Per-fracture, per-image (ie, per-view), and per-study sensitivity and specificity were determined. Area under the receiver operating characteristic curve (AUC) analysis was performed. RESULTS: The model detected and correctly localized 310 (91.2%) of 340 and 236 (96.3%) of 245 of all radius and ulna fractures on the frontal and lateral views, respectively. The per-image sensitivity, specificity, and AUC were 95.7% (95% confidence interval [CI]: 92.4%, 97.8%), 82.5% (95% CI: 77.4%, 86.8%), and 0.918 (95% CI: 0.894, 0.941), respectively, for the frontal view and 96.7% (95% CI: 93.6%, 98.6%), 86.4% (95% CI: 81.9%, 90.2%), and 0.933 (95% CI: 0.912, 0.954), respectively, for the lateral view. The per-study sensitivity, specificity, and AUC were 98.1% (95% CI: 95.6%, 99.4%), 72.9% (95% CI: 67.1%, 78.2%), and 0.895 (95% CI: 0.870, 0.920), respectively. CONCLUSION: The ability of an object detection CNN to detect and localize radius and ulna fractures on wrist radiographs with high sensitivity and specificity was demonstrated.© RSNA, 2019.

Diagnostic utility of whole body CT scanning in patients with unexplained weight loss.

BACKGROUND: Unexplained weight loss is a non-specific complaint with myriad potential etiologies. Increasingly, whole body CT studies are being performed in patients with unexplained weight loss to exclude organic etiologies such as malignancy. Our study aims to assess the diagnostic accuracy and yield of whole body CT in these patients. METHODS AND MATERIALS: Patients who had a whole body CT scan for investigation of unexplained weight loss as their primary complaint from 2009-2012 were retrospectively reviewed. CT scans were classified into 4 categories: (1) Definite/highly suspicious for underlying organic cause (2) Indeterminate for underlying organic cause (3) No findings accounting for weight loss and only incidental findings and (4) Normal study. Scan findings were correlated with the final diagnosis after all investigations. Univariate logistic regression was performed to determine associations between patient's baseline variables and positive CT scan findings. RESULTS: Of 301 eligible patients during the study period, 101 patients were excluded due to known history of malignancy, inadequate follow-up or inadequate scan technique. 200 patients were included in the final analyses. The sensitivity, specificity, positive predictive value, negative predictive value and accuracy of CT for organic pathology were 72.0%, 90.7%, 87.0%, 78.9% and 82.0% respectively. Additional symptoms, abnormal physical examinations, anemia, and raised tumor markers were significantly correlated with positive CT findings. Overall, the diagnostic yield of whole body CT scan for patients with unexplained weight loss was 33.5%. CONCLUSIONS: Whole body CT imaging may be a useful investigation for the noninvasive workup of patients with unexplained weight loss, with diagnostic yield of 33.5% and good sensitivity, specificity, positive and negative predictive values for organic etiologies.

CT features in surgically proven cases of ovarian torsion-a pictorial review.

Ovarian torsion is a surgical emergency characterized by a partial or complete rotation of the ovarian vascular pedicle, causing obstruction to venous outflow followed by arterial inflow. Clinically, ovarian torsion frequently mimics other causes of acute pelvic pain such as appendicitis, diverticulitis, renal colic etc. Ultrasonography is the first-line imaging modality of choice for evaluation of ovarian torsion. MRI is useful as a problem-solving tool in patients with equivocal or non-diagnostic ultrasonography studies. CT is ordinarily not utilized in a young female with suspected ovarian torsion due to the radiation dose. However, the significant expansion in use of CT imaging in emergency departments for female patients presenting with acute abdominal pain has increased the likelihood that ovarian torsion may be first seen on CT. In addition, a non-specific clinical presentation may lead to an initial imaging with CT rather than ultrasonography. Ultrasound features of the ovarian torsion are well known and sufficiently described across literature as compared with the CT scan findings. In view of the increasing usage of CT as the modality of choice in emergency settings, it is imperative for the radiologist to familiarize with the CT features of ovarian torsion. An early correct diagnosis by the radiologist in clinically unsuspected cases, facilitating a prompt surgery to restore the ovarian blood flow can prevent permanent irreversible damage. There is limited published data available on the CT features of ovarian torsion. This pictorial essay illustrates CT findings with histological correlation of surgically proven ovarian torsion in our institution. These patients were primarily investigated with CT scan for acute pelvic pain ascribed to non-gynaecological causes such as bowel or urinary tract lesions.

Diffusion-weighted imaging of gynecologic tumors: diagnostic pearls and potential pitfalls.

Conventional magnetic resonance (MR) imaging has an established role in gynecologic imaging. However, increasing clinical demand for improved lesion characterization and disease mapping to optimize patient management has resulted in the incorporation of newer sequences, such as diffusion-weighted (DW) imaging, into routine protocols for pelvic MR imaging. DW imaging provides functional information about the microenvironment of water in tissues, hence augmenting the morphologic information derived from conventional MR images. It can depict shifts of water from extracellular to intracellular compartments, altered cell membrane permeability, disruption of cell membrane depolarization, and increased cellular density. Such changes may be associated with tumors. DW imaging has emerged as an important cancer biomarker and takes the role of the radiologist from the level of mere macroscopic diagnosis to more active participation in determining patient prognosis and management through a better understanding of the tumor microenvironment. With the growing acknowledgment of DW imaging as a pivotal tool in the radiologist's armamentarium, radiologists must be familiar with the appearances of various gynecologic tumors at DW imaging and understand the implications of this sequence for improving diagnostic accuracy and predicting and monitoring treatment response. Although positron emission tomography/computed tomography is extremely useful for detecting tumor recurrence in cervical and ovarian carcinomas, it has a limited specificity in the immediate posttreatment setting. DW imaging may aid in detection of residual or recurrent tumors in such situations. DW imaging is a potentially useful adjunct to conventional MR imaging for evaluation of gynecologic tumors, thus improving overall diagnostic accuracy, tumor staging, prediction of response to therapy, and treatment follow-up.