Prospective validation of the role of PET/CT in detecting disease after neoadjuvant chemotherapy in advanced ovarian cancer.

OBJECTIVES: The study aimed to compare the diagnostic accuracies of 2-[18F]FDG PET/CT and contrast-enhanced CT (ceCT) after neoadjuvant chemotherapy (NACT) in advanced ovarian cancer (OC). MATERIALS AND METHODS: This study consisted historical observational cohort and prospective validation cohort. Patients with newly diagnosed stage III-IV OC scheduled for NACT were recruited, with imaging performed after three to six cycles of NACT before interval debulking surgery. Nineteen regions in the abdominopelvic cavity were scored for the presence and absence of disease, referenced to the intra-operative findings or histological specimens. Diagnostic metrics were compared using McNemar's test. RESULTS: In the historical cohort (23 patients, age 58 ± 13), 2-[18F]FDG PET had an overall accuracy (Acc) 82%, sensitivity (Sen) 38%, specificity (Spe) 97%, positive predictive value (PPV) 79% and negative predictive value (NPV) 82%; ceCT had an overall Acc 86%, Sen 64%, Spe 93%, PPV 75% and NPV 89%. In the prospective cohort (46 patients, age 59 ± 9), 2-[18F] FDG PET had an overall Acc 87%, Sen 48%, Spe 98%, PPV 84% and NPV 88%; ceCT had an overall Acc 89%, Sen 66%, Spe 95%, PPV 77% and NPV 91%. No significant difference was demonstrated between the two imaging modalities (p > 0.05). High false-negative rates were observed in the right subdiaphragmatic space, omentum, bowel mesentery and serosa. High omental metabolic uptake after NACT was associated with histological non-responders (p < 0.05). CONCLUSION: 2-[18F]FDG PET/CT had no additional value over ceCT with comparable diagnostic accuracy in detecting disease after NACT in advanced OC. CLINICAL RELEVANCE STATEMENT: 2-[18F]FDG PET/CT is not superior to contrast-enhanced CT in determining disease after neoadjuvant chemotherapy in advanced ovarian cancer; contrast-enhanced CT should be suffice for surgical planning before interval debulking surgery. KEY POINTS: • Additional value of 2-[18F]FDG PET/CT over contrast-enhanced CT is undefined in detecting disease after neoadjuvant chemotherapy. • 2-[18F]FDG PET/CT has comparable diagnostic accuracy compared to contrast-enhanced CT. • Contrast-enhanced CT will be suffice for surgical planning after neoadjuvant chemotherapy.

Noninvasive imaging signatures of HER2 and HR using ADC in invasive breast cancer: repeatability, reproducibility, and association with pathological complete response to neoadjuvant chemotherapy.

BACKGROUND: The immunohistochemical test (IHC) of HER2 and HR can provide prognostic information and treatment guidance for invasive breast cancer patients. We aimed to develop noninvasive image signatures ISHER2 and ISHR of HER2 and HR, respectively. We independently evaluate their repeatability, reproducibility, and association with pathological complete response (pCR) to neoadjuvant chemotherapy. METHODS: Pre-treatment DWI, IHC receptor status HER2/HR, and pCR to neoadjuvant chemotherapy of 222 patients from the multi-institutional ACRIN 6698 trial were retrospectively collected. They were pre-separated for development, independent validation, and test-retest. 1316 image features were extracted from DWI-derived ADC maps within manual tumor segmentations. ISHER2 and ISHR were developed by RIDGE logistic regression using non-redundant and test-retest reproducible features relevant to IHC receptor status. We evaluated their association with pCR using area under receiver operating curve (AUC) and odds ratio (OR) after binarization. Their reproducibility was further evaluated using the test-retest set with intra-class coefficient of correlation (ICC). RESULTS: A 5-feature ISHER2 targeting HER2 was developed (AUC = 0.70, 95% CI 0.59 to 0.82) and validated (AUC = 0.72, 95% CI 0.58 to 0.86) with high perturbation repeatability (ICC = 0.92) and test-retest reproducibility (ICC = 0.83). ISHR was developed using 5 features with higher association with HR during development (AUC = 0.75, 95% CI 0.66 to 0.84) and validation (AUC = 0.74, 95% CI 0.61 to 0.86) and similar repeatability (ICC = 0.91) and reproducibility (ICC = 0.82). Both image signatures showed significant associations with pCR with AUC of 0.65 (95% CI 0.50 to 0.80) for ISHER2 and 0.64 (95% CI 0.50 to 0.78) for ISHER2 in the validation cohort. Patients with high ISHER2 were more likely to achieve pCR to neoadjuvant chemotherapy with validation OR of 4.73 (95% CI 1.64 to 13.65, P value = 0.006). Low ISHR patients had higher pCR with OR = 0.29 (95% CI 0.10 to 0.81, P value = 0.021). Molecular subtypes derived from the image signatures showed comparable pCR prediction values to IHC-based molecular subtypes (P value > 0.05). CONCLUSION: Robust ADC-based image signatures were developed and validated for noninvasive evaluation of IHC receptors HER2 and HR. We also confirmed their value in predicting treatment response to neoadjuvant chemotherapy. Further evaluations in treatment guidance are warranted to fully validate their potential as IHC surrogates.

Association between IVIM parameters and treatment response in locally advanced squamous cell cervical cancer treated by chemoradiotherapy.

OBJECTIVE: To examine the associations of intravoxel incoherent motion (IVIM) parameters with treatment response in cervical cancer following concurrent chemoradiotherapy (CCRT). MATERIALS AND METHODS: Forty-five patients, median age of 58 years (range: 28-82), with pre-CCRT and post-CCRT MRI, were retrospectively analysed. The IVIM parameters pure diffusion coefficient (D) and perfusion fraction (f) were estimated using the full b-value distribution (BVD) as well as an optimised subsample BVD. Dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC) were used to measure observer repeatability in tumour delineation at both time points. Treatment response was determined by the response evaluation criteria in solid tumour (RECIST) 1.1 between MRI examinations. Mann-Whitney U tests were used to test for significant differences in IVIM parameters between treatment response groups. RESULTS: Pre-CCRT tumour delineation repeatability was good (DSC = 0.81) while post-CCRT delineation repeatability was moderate (DSC = 0.67). Values of D and f had good repeatability at both time points (ICC > 0.80). Pre-CCRT f estimated using the full BVD and optimised subsample BVD were found to be significantly higher in patients with partial response compared to those with stable disease or disease progression (p = 0.01 and 95% CI = -0.02-0.00 for both cases). CONCLUSION: Pre-CCRT f was associated with treatment response in cervical cancer with good observer repeatability. Similar discriminative ability was also observed in estimated pre-CCRT f from an optimised subsample BVD. KEY POINTS: • Pre-treatment tumour delineation and IVIM parameters had good observer repeatability. • Post-treatment tumour delineation was worse than at pre-treatment, but IVIM parameters retained good ICC. • Pre-treatment perfusion fraction estimated from all b-values and an optimised subsample of b-values were associated with treatment response.

Association between MRI histogram features and treatment response in locally advanced cervical cancer treated by chemoradiotherapy.

OBJECTIVE: To examine the associations of histogram features of T2-weighted (T2W) images and apparent diffusion coefficient (ADC) with treatment response in locally advanced cervical cancer (LACC) following concurrent chemoradiotherapy (CCRT). MATERIALS AND METHODS: Fifty-eight patients who underwent a 4-week CCRT regimen with MRI prior to treatment (pre-CCRT) and after treatment (post-CCRT) were retrospectively analysed. Histogram features were calculated from volumes of interest (VOIs) from one radiologist on T2W images and ADC maps. VOIs from two radiologists were used to assess observer repeatability in delineation and feature values at both time-points with the Dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC). Treatment response was defined as a 90% reduction in tumour volume. Paired Mann-Whitney U tests were used to determine if features changed significantly between examinations. Two-sample Mann-Whitney U tests were used to identify features that were significantly different between response groups. Receiver operating characteristic (ROC) analysis was done on significantly different MRI features between treatment response groups. RESULTS: Pre-CCRT delineation and feature repeatability were generally good (DSC > 0.700; ICC > 0.750). Post-CCRT repeatability was low (DSC < 0.700; ICC < 0.750), but ADC mean and percentiles retained good ICC scores. All features, except for T2WKurtosis, significantly changed between examinations. Post-CCRT ADC50 was the only feature that demonstrated both good observer variability and significant differences between treatment response groups (p = 0.036) and had an AUC of 0.701 with a cut-off of 1.357 × 10-6 mm2/s. CONCLUSION: ADC and T2W histogram features could be used to track changes in LACC tumours undergoing CCRT. Post-CCRT ADC50 was associated with treatment response with good observer repeatability. KEY POINTS: • Pre-treatment tumour delineation and histogram feature values had good observer repeatability, while these were less repeatable at post-treatment. • MRI histogram analysis could be used to track changes in the tumour as it undergoes concurrent chemoradiotherapy. • Post-treatment median ADC was associated with treatment response and had good repeatability.

Frequency and Distribution of Chest Radiographic Findings in Patients Positive for COVID-19.

Background Current coronavirus disease 2019 (COVID-19) radiologic literature is dominated by CT, and a detailed description of chest radiography appearances in relation to the disease time course is lacking. Purpose To describe the time course and severity of findings of COVID-19 at chest radiography and correlate these with real-time reverse transcription polymerase chain reaction (RT-PCR) testing for severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2, nucleic acid. Materials and Methods This is a retrospective study of patients with COVID-19 confirmed by using RT-PCR and chest radiographic examinations who were admitted across four hospitals and evaluated between January and March 2020. Baseline and serial chest radiographs (n = 255) were reviewed with RT-PCR. Correlation with concurrent CT examinations (n = 28) was performed when available. Two radiologists scored each chest radiograph in consensus for consolidation, ground-glass opacity, location, and pleural fluid. A severity index was determined for each lung. The lung scores were summed to produce the final severity score. Results The study was composed of 64 patients (26 men; mean age, 56 years ± 19 [standard deviation]). Of these, 58 patients had initial positive findings with RT-PCR (91%; 95% confidence interval: 81%, 96%), 44 patients had abnormal findings at baseline chest radiography (69%; 95% confidence interval: 56%, 80%), and 38 patients had initial positive findings with RT-PCR testing and abnormal findings at baseline chest radiography (59%; 95% confidence interval: 46%, 71%). Six patients (9%) showed abnormalities at chest radiography before eventually testing positive for COVID-19 with RT-PCR. Sensitivity of initial RT-PCR (91%; 95% confidence interval: 83%, 97%) was higher than that of baseline chest radiography (69%; 95% confidence interval: 56%, 80%) (P = .009). Radiographic recovery (mean, 6 days ± 5) and virologic recovery (mean, 8 days ± 6) were not significantly different (P = .33). Consolidation was the most common finding (30 of 64; 47%) followed by ground-glass opacities (21 of 64; 33%). Abnormalities at chest radiography had a peripheral distribution (26 of 64; 41%) and lower zone distribution (32 of 64; 50%) with bilateral involvement (32 of 64; 50%). Pleural effusion was uncommon (two of 64; 3%). The severity of findings at chest radiography peaked at 10-12 days from the date of symptom onset. Conclusion Findings at chest radiography in patients with coronavirus disease 2019 frequently showed bilateral lower zone consolidation, which peaked at 10-12 days from symptom onset. © RSNA, 2020.

Qualitative and quantitative comparison of image quality between single-shot echo-planar and interleaved multi-shot echo-planar diffusion-weighted imaging in female pelvis.

OBJECTIVES: To qualitatively and quantitatively compare the image quality between single-shot echo-planar (SS-EPI) and multi-shot echo-planar (IMS-EPI) diffusion-weighted imaging (DWI) in female pelvis METHODS: This was a prospective study involving 80 females who underwent 3.0T pelvic magnetic resonance imaging (MRI). SS-EPI and IMS-EPI DWI were acquired with 3 b values (0, 400, 800 s/mm2). Two independent reviewers assessed the overall image quality, artifacts, sharpness, and lesion conspicuity based on a 5-point Likert scale. Regions of interest (ROI) were placed on the endometrium and the gluteus muscles to quantify the signal intensities and apparent diffusion coefficient (ADC). Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and geometric distortion were quantified on both sequences. Inter-rater agreement was assessed using κ statistics and Kendall test. Qualitative scores were compared using Wilcoxon signed-rank test and quantitative parameters were compared with paired t test and Bland-Altman analysis. RESULTS: IMS-EPI demonstrated better image quality than SS-EPI for all aspects evaluated (SS-EPI vs. IMS-EPI: overall quality 3.04 vs. 4.17, artifacts 3.09 vs. 3.99, sharpness 2.40 vs. 4.32, lesion conspicuity 3.20 vs. 4.25; p < 0.001). Good agreement and correlation were observed between two reviewers (SS-EPI κ 0.699, r 0.742; IMS-EPI κ 0.702, r 0.789). IMS-EPI showed lower geometric distortion, SNR, and CNR than SS-EPI (p < 0.050). There was no significant difference in the mean ADC between the two sequences. CONCLUSION: IMS-EPI showed better image quality with lower geometric distortion without affecting the quantification of ADC, though the SNR and CNR decreased due to post-processing limitations. KEY POINTS: • IMS-EPI showed better image quality than SS-EPI. • IMS-EPI showed lower geometric distortion without affecting ADC compared with SS-EPI. • The SNR and CNR of IMS-EPI decreased due to post-processing limitations.

Intravoxel incoherent motion MRI assessment of chemoradiation-induced pelvic bone marrow changes in cervical cancer and correlation with hematological toxicity.

PURPOSE: To investigate bone marrow changes after chemoradiation (CRT) using intravoxel incoherent motion magnetic resonance imaging (IVIM-MRI) and correlate imaging changes with hematological toxicity (HT) in patients with locally advanced cervical cancer. MATERIALS AND METHODS: Thirty-nine patients with newly diagnosed cervical cancer were prospectively recruited for two sequential 3.0T IVIM-MRI studies: before treatment (MRI-1) and 3-4 weeks after standardized CRT (MRI-2). The irradiated pelvic bone marrow was outlined as the regions of interest to derive the true diffusion coefficient (D) and perfusion fraction (f) based on a biexponential model. The apparent coefficient diffusion (ADC) was derived using the monoexponential model. Changes in these parameters between MRI-1 and MRI-2 were calculated as ΔD, Δf, and ΔADC. HT was defined accordingly to NCI-CTCAE (v. 4.03) of grade 3 and above. Statistical analysis was performed using Mann-Whitney U-test. RESULTS: The median age of patients was 54 years old (range 27-83 years old); 14 patients suffered from HT. Early bone marrow changes (3-4 weeks) of ΔD showed a significant difference between HT and non-HT groups (6.4 ± 19.7% vs. -6.4 ± 19.4%, respectively, P = 0.041). However, no significant changes were noted in Δf (3.7 ± 13.3% vs. 1.5 ± 12.5% respectively, P = 0. 592) and ΔADC (5.5 ± 26.3% vs. -3.3 ± 27.0% respectively, P = 0.303) between the HT and non-HT groups. Δf increased insignificantly for both groups. CONCLUSION: ΔD was the only significant parameter to differentiate early cellular environment changes in bone marrow after CRT, suggestive that ΔD was more sensitive than Δf and ΔADC to reflect the underlying microenvironment injury. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1491-1498.

Successful Treatment of a Pararenal Pregnancy Using High-Dose Methotrexate Regimen: A Case Report.

Background: Upper abdominal pregnancy is rare. Most patients present with hemoperitoneum, requiring emergency laparotomy. Case: A 32-year-old woman presented with acute abdominal pain and an elevated beta-human chorionic gonadotropin (ß-hCG) level. Ultrasound, computerized tomography (CT) scans, and laparoscopy failed to locate the source of elevated hCG. Subsequent positron emission tomography (PET)-CT demonstrated a cystic mass in the left pararenal region with no increased uptake. Repeated ultrasound scan revealed a live fetus implanted laterally to the abdominal aorta. After failing to respond to methotrexate at the usual dosage, a regimen used in gestational trophoblastic neoplasia was given. The pregnancy underwent miscarriage afterwards, and the hCG level gradually returned to normal. Conclusion: The site of an ectopic pregnancy should be sought thoroughly to avoid missing an abdominal pregnancy and hence disastrous hemoperitoneum. While medical therapy with high-dose methotrexate is not a standard treatment, it can be considered after failing the traditional therapy, provided that there is adequate treatment monitoring and expertise in handling the side effects of the medication.

Relationship between intravoxel incoherent motion diffusion-weighted MRI and dynamic contrast-enhanced MRI in tissue perfusion of cervical cancers.

PURPOSE: To investigate the relationship between intravoxel incoherent motion (IVIM) diffusion-weighted magnetic resonance imaging (MRI) and dynamic contrast-enhanced MRI (DCE-MRI) in cervical cancer perfusion. MATERIALS AND METHODS: Prospective newly diagnosed cervical cancer patients underwent diffusion-weighted MRI (13 b-values: 1-1000 s/mm(2) ) and DCE-MRI. The IVIM perfusion parameters, perfusion fraction (f), pseudodiffusion coefficient (D*), and flow-related parameter (fD*), were derived from a biexponential decay model. DCE-MRI was analyzed with a pharmacokinetic model and signal-time curve to derive the amplitude factor (A), estimated volume transfer constant between blood plasma, and the extravascular extracellular space (est K(trans) ), maximum relative enhancement (MaxRE), and area under the signal-time curve (AUC). Spearman's rank correlation coefficient (r) evaluated the correlative relationships. RESULTS: The f = 13.51% ± 1.76%, D* = 71.72 ± 7.55 × 10(-3) mm(2) /s, fD* = 9.64 ± 1.28 × 10(-3) mm(2) /s, A = 1.41 ± 0.43, est K(trans) = 0.19 ± 0.06 s(-1) , MaxRE of 120.02 ± 21.07%, and AUC 212,393 ± 54,423 was found in 25 cervical cancer patients. Statistically significant positive correlations were found between fD* and est K(trans) (r = 0.42, P = 0.038), fD* and A (r = 0.50, P = 0.011), fD* and MaxRE (r = 0.52, P = 0.008), f and AUC (r = 0.58, P = 0.003). CONCLUSION: The IVIM perfusion parameters showed moderate to good correlations with quantitative and semiquantitative perfusion parameters derived from DCE-MRI in cervical cancer.

Correlation between tissue metabolism and cellularity assessed by standardized uptake value and apparent diffusion coefficient in peritoneal metastasis.

PURPOSE: To evaluate the correlation between standardized uptake value (SUV) (tissue metabolism) and apparent diffusion coefficient (ADC) (water diffusivity) in peritoneal metastases. MATERIALS AND METHODS: Patients with peritoneal dissemination detected on (18)F-fluorodeoxyglucose positron emission tomography combined with computed tomography (FDG-PET/CT) were prospectively recruited for MRI examinations with informed consent and the study was approved by the local Institutional Review Board. FDG-PET/CT, diffusion-weighted imaging (DWI), MRI, and DWI/MRI images were independently reviewed by two radiologists based on visual analysis. SUVmax/SUVmean and ADCmin/ADCmean were obtained manually by drawing ROIs over the peritoneal metastases on FDG-PET/CT and DWI, respectively. Diagnostic characteristics of each technique were evaluated. Pearson's coefficient and McNemar and Kappa tests were used for statistical analysis. RESULTS: Eight patients were recruited for this prospective study and 34 peritoneal metastases were evaluated. ADCmean was significantly and negatively correlated with SUVmax (r = -0.528, P = 0.001) and SUVmean (r = -0.548, P = 0.001). ADCmin had similar correlation with SUVmax (r = -0.508, P = 0.002) and SUVmean (r = -0.513, P = 0.002). DWI/MRI had high diagnostic performance (accuracy = 98%) comparable to FDG-PET/CT, in peritoneal metastasis detection. Kappa values were excellent for all techniques. CONCLUSION: There was a significant inverse correlation between SUV and ADC.

Perfusion and diffusion characteristics of cervical cancer based on intraxovel incoherent motion MR imaging-a pilot study.

OBJECTIVES: To investigate the tissue characteristics of cervical cancer based on the intravoxel incoherent motion (IVIM) model and to assess the IVIM parameters in tissue differentiation in the female pelvis. METHODS: Sixteen treatment-naïve cervical cancer and 17 age-matched healthy subjects were prospectively recruited for diffusion-weighted (b = 0-1,000 s/mm(2)) and standard pelvic MRI. Bi-exponential analysis was performed to derive the perfusion parameters f (perfusion fraction) and D* (pseudodiffusion coefficient) as well as the diffusion parameter D (true molecular diffusion coefficient) in cervical cancer (n = 16), normal cervix (n = 17), myometrium (n = 33) and leiomyoma (n = 14). Apparent diffusion coefficient (ADC) was calculated. Kruskal-Wallis test and receiver operating characteristics (ROC) curves were used. RESULTS: Cervical cancer had the lowest f (14.9 ± 2.6%) and was significantly different from normal cervix and leiomyoma (p < 0.05). The D (0.86 ± 0.16 x 10(-3) mm2/s) was lowest in cervical cancer and was significantly different from normal cervix and myometrium (p < 0.05) but not leiomyoma. No difference was observed in D*. D was consistently lower than ADC in all tissues. ROC curves indicated that f < 16.38%, D < 1.04 × 10(-3) mm(2)/s and ADC < 1.13 × 10(-3) mm(2)/s could differentiate cervical cancer from non-malignant tissues (AUC 0.773-0.908). CONCLUSIONS: Cervical cancer has low perfusion and diffusion IVIM characteristics with promising potential for tissue differentiation. KEY POINTS: • Diffusion-weighted MRI is increasingly applied in evaluation of cervical cancer. • Cervical cancer has distinctive perfusion and diffusion characteristics. • Intravoxel incoherent motion characteristics can differentiate cervical cancer from non-malignant uterine tissues.

PET/Computed Tomography Transformation of Oncology: Ovarian Cancers.

Over the last quarter of a century, fluorine-18-fluorodeoxyglucose (FDG) PET/computed tomography (CT) has revolutionized the diagnostic algorithm of ovarian cancer, impacting on the initial disease evaluation including staging and surgical planning, treatment response assessment and prognostication, to the most important role in detection of recurrent disease. The role of FDG PET/CT is expanding with the adoption of new therapeutic agents. Other non-FDG tracers have been explored with fibroblast activation protein inhibitor being promising. Novel tracers may provide the basis for future theragnostic work. This article will review the evolution and impact of PET/CT in ovarian cancer management.

Deep learning for predicting epidermal growth factor receptor mutations of non-small cell lung cancer on PET/CT images.

Background: Predicting the mutation status of the epidermal growth factor receptor (EGFR) gene based on an integrated positron emission tomography/computed tomography (PET/CT) image of non-small cell lung cancer (NSCLC) is a noninvasive, low-cost method which is valuable for targeted therapy. Although deep learning has been very successful in robotic vision, it is still challenging to predict gene mutations in PET/CT-derived studies because of the small amount of medical data and the different parameters of PET/CT devices. Methods: We used the advanced EfficientNet-V2 model to predict the EGFR mutation based on fused PET/CT images. First, we extracted 3-dimensional (3D) pulmonary nodules from PET and CT as regions of interest (ROIs). We then fused each single PET and CT image. The network model was used to predict the mutation status of lung nodules by the new data after fusion, and the model was weighted adaptively. The EfficientNet-V2 model used multiple channels to represent nodules comprehensively. Results: We trained the EfficientNet-V2 model through our PET/CT fusion algorithm using a dataset of 150 patients. The prediction accuracy of EGFR and non-EGFR mutations was 86.25% in the training dataset, and the accuracy rate was 81.92% in the validation set. Conclusions: Combined with experiments, the demonstrated PET/CT fusion algorithm outperformed radiomics methods in predicting EGFR and non-EGFR mutations in NSCLC.

A deep learning model based on the attention mechanism for automatic segmentation of abdominal muscle and fat for body composition assessment.

Background: Quantitative muscle and fat data obtained through body composition analysis are expected to be a new stable biomarker for the early and accurate prediction of treatment-related toxicity, treatment response, and prognosis in patients with lung cancer. The use of these biomarkers can enable the adjustment of individualized treatment regimens in a timely manner, which is critical to further improving patient prognosis and quality of life. We aimed to develop a deep learning model based on attention for fully automated segmentation of the abdomen from computed tomography (CT) to quantify body composition. Methods: A fully automatic segmentation deep learning model was designed based on the attention mechanism and using U-Net as the framework. Subcutaneous fat, skeletal muscle, and visceral fat were manually segmented by two experts to serve as ground truth labels. The performance of the model was evaluated using Dice similarity coefficients (DSCs) and Hausdorff distance at 95th percentile (HD95). Results: The mean DSC for subcutaneous fat and skeletal muscle were high for both the enhanced CT test set (0.93±0.06 and 0.96±0.02, respectively) and the plain CT test set (0.90±0.09 and 0.95±0.01, respectively). Nevertheless, the model did not perform well in the segmentation performance of visceral fat, especially for the enhanced CT test set. The mean DSC for the enhanced CT test set was 0.87±0.11, while the mean DSC for the plain CT test set was 0.92±0.03. We discuss the reasons for this result. Conclusions: This work demonstrates a method for the automatic outlining of subcutaneous fat, skeletal muscle, and visceral fat areas at L3.

A classifier-combined method for grading breast cancer based on Dempster-Shafer evidence theory.

Background: Preoperative non-invasive histologic grading of breast cancer is essential. This study aimed to explore the effectiveness of a machine learning classification method based on Dempster-Shafer (D-S) evidence theory for the histologic grading of breast cancer. Methods: A total of 489 contrast-enhanced magnetic resonance imaging (MRI) slices with breast cancer lesions (including 171 grade Ⅰ, 140 grade Ⅱ, and 178 grade Ⅲ lesions) were used for analysis. All the lesions were segmented by two radiologists in consensus. For each slice, the quantitative pharmacokinetic parameters based on a modified Tofts model and the textural features of the segmented lesion on the image were extracted. Principal component analysis was then used to reduce feature dimensionality and obtain new features from the pharmacokinetic parameters and texture features. The basic confidence assignments of different classifiers were combined using D-S evidence theory based on the accuracy of three classifiers: support vector machine (SVM), Random Forest, and k-nearest neighbor (KNN). The performance of the machine learning techniques was evaluated in terms of accuracy, sensitivity, specificity, and the area under the curve. Results: The three classifiers showed varying accuracy across different categories. The accuracy of using D-S evidence theory in combination with multiple classifiers reached 92.86%, which was higher than that of using SVM (82.76%), Random Forest (78.85%), or KNN (87.82%) individually. The average area under the curve of using the D-S evidence theory combined with multiple classifiers reached 0.896, which was larger than that of using SVM (0.829), Random Forest (0.727), or KNN (0.835) individually. Conclusions: Multiple classifiers can be effectively combined based on D-S evidence theory to improve the prediction of histologic grade in breast cancer.

ChatGPT versus human in generating medical graduate exam multiple choice questions-A multinational prospective study (Hong Kong S.A.R., Singapore, Ireland, and the United Kingdom).

INTRODUCTION: Large language models, in particular ChatGPT, have showcased remarkable language processing capabilities. Given the substantial workload of university medical staff, this study aims to assess the quality of multiple-choice questions (MCQs) produced by ChatGPT for use in graduate medical examinations, compared to questions written by university professoriate staffs based on standard medical textbooks. METHODS: 50 MCQs were generated by ChatGPT with reference to two standard undergraduate medical textbooks (Harrison's, and Bailey & Love's). Another 50 MCQs were drafted by two university professoriate staff using the same medical textbooks. All 100 MCQ were individually numbered, randomized and sent to five independent international assessors for MCQ quality assessment using a standardized assessment score on five assessment domains, namely, appropriateness of the question, clarity and specificity, relevance, discriminative power of alternatives, and suitability for medical graduate examination. RESULTS: The total time required for ChatGPT to create the 50 questions was 20 minutes 25 seconds, while it took two human examiners a total of 211 minutes 33 seconds to draft the 50 questions. When a comparison of the mean score was made between the questions constructed by A.I. with those drafted by humans, only in the relevance domain that the A.I. was inferior to humans (A.I.: 7.56 +/- 0.94 vs human: 7.88 +/- 0.52; p = 0.04). There was no significant difference in question quality between questions drafted by A.I. versus humans, in the total assessment score as well as in other domains. Questions generated by A.I. yielded a wider range of scores, while those created by humans were consistent and within a narrower range. CONCLUSION: ChatGPT has the potential to generate comparable-quality MCQs for medical graduate examinations within a significantly shorter time.

Utility of FDG PET/CT in the assessment of myeloid sarcoma.

OBJECTIVE: Myeloid sarcoma (MS) is a rare extramedullary manifestation of acute myeloid leukemia that often presents during remission or disease relapse. With awareness of this clinical entity and the appropriate clinical history, MS can be detected despite its nonspecific radiologic features. CONCLUSION: This article highlights the utility of (18)F-FDG PET/CT, which has high sensitivity in detecting early MS and provides a systemic overview of tumor burden, and its potential role in monitoring of treatment response.

Intraoperative Frozen Section Biopsy of Uterine Smooth Muscle Tumors: A Clinicopathologic Analysis of 112 Cases With Emphasis on Potential Diagnostic Pitfalls.

Frozen sections of uterine smooth muscle tumors are infrequently required, and related diagnostic difficulties are seldom discussed. We analyzed the clinicopathologic features of 112 frozen sections of uterine smooth muscle tumors and determined the accuracy, reasons for deferrals, and causes of interpretational errors. Most patients (median age, 45 y) presented with pelvic mass symptoms (53%). The main reasons for a frozen section examination were an abnormal gross appearance including loss of the usual whorled pattern of leiomyoma (36 cases, 32.1%), and intraoperative discovery of an abnormal growth pattern and extrauterine extension of a uterine tumor (28 cases, 25%). There were 9 leiomyosarcomas and 103 leiomyomas, including 18 benign histologic variants. An accurate diagnosis of malignancy was achieved in all leiomyosarcomas, with the exception of a myxoid leiomyosarcoma. In 99 cases (88%), the frozen section diagnosis concurred with the permanent section diagnosis (false positives, 0.9%; false negatives, 0%). Misinterpretation of stromal hyalinization as tumor cell necrosis in a leiomyoma with amianthoid-like fibers was a major discrepancy. Two minor discrepancies did not lead to a change in management. The diagnosis was deferred in 10 cases (8.9%) because of stromal alterations, unusual cellular morphology, uncertain type of necrosis, and abnormal growth patterns. Thus, although various stromal and cellular alterations can cause diagnostic uncertainty, leading to deferrals, frozen section diagnosis of uterine smooth muscle tumors has a high accuracy rate. While a definitive frozen section diagnosis of malignancy may be made when there is unequivocal atypia, indisputable mitotic figures, and tumor cell necrosis, it is important to remember that nonmyogenic mesenchymal tumors may also mimic uterine smooth muscle tumors. In a frozen section setting, it would be sufficient to issue a diagnosis of "malignant mesenchymal tumor." For tumors that do not meet the criteria for malignancy, issuing a frozen section diagnosis of "atypical mesenchymal tumor and defer the histologic subtyping to the permanent sections" is appropriate.

Diffusion-weighted magnetic resonance imaging of primary cervical cancer in the detection of sub-centimetre metastatic lymph nodes.

BACKGROUND: Magnetic resonance imaging (MRI) has limited accuracy in detecting pelvic lymph node (PLN) metastasis. This study aimed to examine the use of intravoxel incoherent motion (IVIM) in classifying pelvic lymph node (PLN) involvement in cervical cancer patients. METHODS: Fifty cervical cancer patients with pre-treatment magnetic resonance imaging (MRI) were examined for PLN involvement by one subspecialist and one non-subspecialist radiologist. PLN status was confirmed by positron emission tomography or histology. The tumours were then segmented by both radiologists. Kruskal-Wallis tests were used to test for differences between diffusion tumour volume (DTV), apparent diffusion coefficient (ADC), pure diffusion coefficient (D), and perfusion fraction (f) in patients with no malignant PLN involvement, those with sub-centimetre and size-significant PLN metastases. These parameters were then considered as classifiers for PLN involvement, and were compared with the accuracies of radiologists. RESULTS: Twenty-one patients had PLN involvement of which 10 had sub-centimetre metastatic PLNs. DTV increased (p = 0.013) while ADC (p = 0.015), and f (p = 0.006) decreased as the nodal status progressed from no malignant involvement to sub-centimetre and then size-significant PLN metastases. In determining PLN involvement, a classification model (DTV + f) had similar accuracies (80%) as the non-subspecialist (76%; p = 0.73) and subspecialist (90%; p = 0.31). However, in identifying patients with sub-centimetre PLN metastasis, the model had higher accuracy (90%) than the non-subspecialist (30%; p = 0.01) but had similar accuracy with the subspecialist (90%, p = 1.00). Interobserver variability in tumour delineation did not significantly affect the performance of the classification model. CONCLUSION: IVIM is useful in determining PLN involvement but the added value decreases with reader experience.

B-Value Optimization in the Estimation of Intravoxel Incoherent Motion Parameters in Patients with Cervical Cancer.

OBJECTIVE: This study aimed to find the optimal number of b-values for intravoxel incoherent motion (IVIM) imaging analysis, using simulated and in vivo data from cervical cancer patients. MATERIALS AND METHODS: Simulated data were generated using literature pooled means, which served as reference values for simulations. In vivo data from 100 treatment-naïve cervical cancer patients with IVIM imaging (13 b-values, scan time, 436 seconds) were retrospectively reviewed. A stepwise b-value fitting algorithm calculated optimal thresholds. Feed forward selection determined the optimal subsampled b-value distribution for biexponential IVIM fitting, and simplified IVIM modeling using monoexponential fitting was attempted. IVIM parameters computed using all b-values served as reference values for in vivo data. RESULTS: In simulations, parameters were accurately estimated with six b-values, or three b-values for simplified IVIM, respectively. In vivo data showed that the optimal threshold was 40 s/mm² for patients with squamous cell carcinoma and a subsampled acquisition of six b-values (scan time, 198 seconds) estimated parameters were not significantly different from reference parameters (individual parameter error rates of less than 5%). In patients with adenocarcinoma, the optimal threshold was 100 s/mm², but an optimal subsample could not be identified. Irrespective of the histological subtype, only three b-values were needed for simplified IVIM, but these parameters did not retain their discriminative ability. CONCLUSION: Subsampling of six b-values halved the IVIM scan time without significant losses in accuracy and discriminative ability. Simplified IVIM is possible with only three b-values, at the risk of losing diagnostic information.