CT-based deep learning segmentation of ovarian cancer and the stability of the extracted radiomics features.

Background: Radiomics analysis could provide complementary tissue characterization in ovarian cancer (OC). However, OC segmentation required in radiomics analysis is time-consuming and labour-intensive. In this study, we aim to evaluate the performance of deep learning-based segmentation of OC on contrast-enhanced CT images and the stability of radiomics features extracted from the automated segmentation. Methods: Staging abdominopelvic CT images of 367 patients with OC were retrospectively recruited. The training and cross-validation sets came from center A (n=283), and testing set (n=84) came from centers B and C. The tumours were manually delineated by a board-certified radiologist. Four model architectures provided by no-new-Net (nnU-Net) method were tested in this task. The segmentation performance evaluated by Dice score, Jaccard score, sensitivity and precision were compared among 4 architectures. The Pearson correlation coefficient (ρ), concordance correlation coefficient (ρc) and Bland-Altman plots were used to evaluate the volumetric assessment of OC between manual and automated segmentations. The stability of extracted radiomics features was evaluated by intraclass correlation coefficient (ICC). Results: The 3D U-Net cascade architecture achieved highest median Dice score, Jaccard score, sensitivity and precision for OC segmentation in the testing set, 0.941, 0.890, 0.973 and 0.925, respectively. Tumour volumes of manual and automated segmentations were highly correlated (ρ=0.944 and ρc =0.933). 85.0% of radiomics features had high correlation with ICC >0.8. Conclusions: The presented deep-learning segmentation could provide highly accurate automated segmentation of OC on CT images with high stability of the extracted radiomics features, showing the potential as a batch-processing segmentation tool.

Utility of pre-treatment 18 F-fluorodeoxyglucose PET radiomic analysis in assessing nodal involvement in cervical cancer.

OBJECTIVE: Intratumor heterogeneity has prognostic value in cervical cancer, which can be depicted on 18 F-fluorodeoxyglucose ( 18 F-FDG) PET/computed tomography (PET/CT) and then quantitatively characterized by texture features. This study aimed to evaluate the discriminative performance and predictive ability of the texture features in determining lymph node involvement in cervical cancer. METHODS: A total of 101 patients with newly diagnosed cervical cancer, who underwent pre-treatment whole-body 18 F-FDG PET/CT imaging were retrospectively recruited. Patients were categorized based on their nodal status. Thirty-five radiomic features together with the maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of the primary cervical tumors were extracted. Conventional indices were used to build logistic regression model and texture features were used to build random forest model. The performances for differentiating nodal status were assessed by receiver operating characteristic analysis. RESULTS: Conventional PET indices were significantly higher in patients with nodal involvement compared to those without: SUVmax = 14.22 vs. 10.05; MTV = 57.02 vs. 28.73; TLG = 492.8 vs. 188.8 ( P < 0.05). Nineteen radiomic features describing regional heterogeneity were significantly different between nodal involvements. Area under the curves of the models with conventional indices and PET texture features for discriminating nodal status were 0.72 and 0.76, respectively. CONCLUSION: PET-derived radiomic features had moderate performance in discriminating nodal involvement in cervical cancer; and they did not outperform model based on conventional indices.

Tissue Characteristics of Endometrial Carcinoma Analyzed by Quantitative Synthetic MRI and Diffusion-Weighted Imaging.

BACKGROUND: This study investigates the association of T1, T2, proton density (PD) and the apparent diffusion coefficient (ADC) with histopathologic features of endometrial carcinoma (EC). METHODS: One hundred and nine EC patients were prospectively enrolled from August 2019 to December 2020. Synthetic magnetic resonance imaging (MRI) was acquired through one acquisition, in addition to diffusion-weighted imaging (DWI) and other conventional sequences using 1.5T MRI. T1, T2, PD derived from synthetic MRI and ADC derived from DWI were compared among different histopathologic features, namely the depth of myometrial invasion (MI), tumor grade, cervical stromal invasion (CSI) and lymphovascular invasion (LVSI) of EC by the Mann-Whitney U test. Classification models based on the significant MRI metrics were constructed with their respective receiver operating characteristic (ROC) curves, and their micro-averaged ROC was used to evaluate the overall performance of these significant MRI metrics in determining aggressive histopathologic features of EC. RESULTS: EC with MI had significantly lower T2, PD and ADC than those without MI (p = 0.007, 0.006 and 0.043, respectively). Grade 2-3 EC and EC with LVSI had significantly lower ADC than grade 1 EC and EC without LVSI, respectively (p = 0.005, p = 0.020). There were no differences in the MRI metrics in EC with or without CSI. Micro-averaged ROC of the three models had an area under the curve of 0.83. CONCLUSIONS: Synthetic MRI provided quantitative metrics to characterize EC with one single acquisition. Low T2, PD and ADC were associated with aggressive histopathologic features of EC, offering excellent performance in determining aggressive histopathologic features of EC.

Computed Tomographic Radiomics in Differentiating Histologic Subtypes of Epithelial Ovarian Carcinoma.

Importance: Epithelial ovarian carcinoma is heterogeneous and classified according to the World Health Organization Tumour Classification, which is based on histologic features and molecular alterations. Preoperative prediction of the histologic subtypes could aid in clinical management and disease prognostication. Objective: To assess the value of radiomics based on contrast-enhanced computed tomography (CT) in differentiating histologic subtypes of epithelial ovarian carcinoma in multicenter data sets. Design, Setting, and Participants: In this diagnostic study, 665 patients with histologically confirmed epithelial ovarian carcinoma were retrospectively recruited from 4 centers (Hong Kong, Guangdong Province of China, and Seoul, South Korea) between January 1, 2012, and February 28, 2022. The patients were randomly divided into a training cohort (n = 532) and a testing cohort (n = 133) with a ratio of 8:2. This process was repeated 100 times. Tumor segmentation was manually delineated on each section of contrast-enhanced CT images to encompass the entire tumor. The Mann-Whitney U test and voted least absolute shrinkage and selection operator were performed for feature reduction and selection. Selected features were used to build the logistic regression model for differentiating high-grade serous carcinoma and non-high-grade serous carcinoma. Exposures: Contrast-enhanced CT-based radiomics. Main Outcomes and Measures: Intraobserver and interobserver reproducibility of tumor segmentation were measured by Dice similarity coefficients. The diagnostic efficiency of the model was assessed by receiver operating characteristic curve and area under the curve. Results: In this study, 665 female patients (mean [SD] age, 53.6 [10.9] years) with epithelial ovarian carcinoma were enrolled and analyzed. The Dice similarity coefficients of intraobserver and interobserver were all greater than 0.80. Twenty radiomic features were selected for modeling. The areas under the curve of the logistic regression model in differentiating high-grade serous carcinoma and non-high-grade serous carcinoma were 0.837 (95% CI, 0.835-0.838) for the training cohort and 0.836 (95% CI, 0.833-0.840) for the testing cohort. Conclusions and Relevance: In this diagnostic study, radiomic features extracted from contrast-enhanced CT were useful in the classification of histologic subtypes in epithelial ovarian carcinoma. Intraobserver and interobserver reproducibility of tumor segmentation was excellent. The proposed logistic regression model offered excellent discriminative ability among histologic subtypes.

Association between High Diffusion-Weighted Imaging-Derived Functional Tumor Burden of Peritoneal Carcinomatosis and Overall Survival in Patients with Advanced Ovarian Carcinoma.

OBJECTIVE: To investigate the association between functional tumor burden of peritoneal carcinomatosis (PC) derived from diffusion-weighted imaging (DWI) and overall survival in patients with advanced ovarian carcinoma (OC). MATERIALS AND METHODS: This prospective study was approved by the local research ethics committee, and informed consent was obtained. Fifty patients (mean age ± standard deviation, 57 ± 12 years) with stage III-IV OC scheduled for primary or interval debulking surgery (IDS) were recruited between June 2016 and December 2021. DWI (b values: 0, 400, and 800 s/mm²) was acquired with a 16-channel phased-array torso coil. The functional PC burden on DWI was derived based on K-means clustering to discard fat, air, and normal tissue. A score similar to the surgical peritoneal cancer index was assigned to each abdominopelvic region, with additional scores assigned to the involvement of critical sites, denoted as the functional peritoneal cancer index (fPCI). The apparent diffusion coefficient (ADC) of the largest lesion was calculated. Patients were dichotomized by immediate surgical outcome into high- and low-risk groups (with and without residual disease, respectively) with subsequent survival analysis using the Kaplan-Meier curve and log-rank test. Multivariable Cox proportional hazards regression was used to evaluate the association between DWI-derived results and overall survival. RESULTS: Fifteen (30.0%) patients underwent primary debulking surgery, and 35 (70.0%) patients received neoadjuvant chemotherapy followed by IDS. Complete tumor debulking was achieved in 32 patients. Patients with residual disease after debulking surgery had reduced overall survival (p = 0.043). The fPCI/ADC was negatively associated with overall survival when accounted for clinicopathological information with a hazard ratio of 1.254 for high fPCI/ADC (95% confidence interval, 1.007-1.560; p = 0.043). CONCLUSION: A high DWI-derived functional tumor burden was associated with decreased overall survival in patients with advanced OC.

MRI-derived radiomics analysis improves the noninvasive pretreatment identification of multimodality therapy candidates with early-stage cervical cancer.

OBJECTIVES: To develop and validate a clinical-radiomics model that incorporates radiomics signatures and pretreatment clinicopathological parameters to identify multimodality therapy candidates among patients with early-stage cervical cancer. METHODS: Between January 2017 and February 2021, 235 patients with IB1-IIA1 cervical cancer who underwent radical hysterectomy were enrolled and divided into training (n = 194, training:validation = 8:2) and testing (n = 41) sets according to surgical time. The radiomics features of each patient were extracted from preoperative sagittal T2-weighted images. Significance testing, Pearson correlation analysis, and Least Absolute Shrinkage and Selection Operator were used to select radiomic features associated with multimodality therapy administration. A clinical-radiomics model incorporating radiomics signature, age, 2018 Federation International of Gynecology and Obstetrics (FIGO) stage, menopausal status, and preoperative biopsy histological type was developed to identify multimodality therapy candidates. A clinical model and a clinical-conventional radiological model were also constructed. A nomogram and decision curve analysis were developed to facilitate clinical application. RESULTS: The clinical-radiomics model showed good predictive performance, with an area under the curve, sensitivity, and specificity in the testing set of 0.885 (95% confidence interval: 0.781-0.989), 78.9%, and 81.8%, respectively. The AUC, sensitivity, and specificity of the clinical model and clinical-conventional radiological model were 0.751 (0.603-0.900), 63.2%, and 63.6%, 0.801 (0.661-0.942), 73.7%, and 68.2%, respectively. A decision curve analysis demonstrated that when the threshold probability was > 20%, the clinical-radiomics model or nomogram may be more advantageous than the treat all or treat-none strategy. CONCLUSIONS: The clinical-radiomics model and nomogram can potentially identify multimodality therapy candidates in patients with early-stage cervical cancer. KEY POINTS: • Pretreatment identification of multimodality therapy candidates among patients with early-stage cervical cancer helped to select the optimal primary treatment and reduce severe complication risk and costs. • The clinical-radiomics model achieved a better prediction performance compared with the clinical model and the clinical-conventional radiological model. • An easy-to-use nomogram exhibited good performance for individual preoperative prediction.

Radiomic Features of T2-weighted Imaging and Diffusion Kurtosis Imaging in Differentiating Clinicopathological Characteristics of Cervical Carcinoma.

RATIONALE AND OBJECTIVES: Clinicopathological characteristics including histological subtypes, tumour grades and International Federation of Gynecology and Obstetrics (FIGO) stages are crucial factors in the clinical decision for cervical carcinoma (CC). The purpose of this study was to evaluate the ability of T2-weighted imaging (T2WI) and diffusion kurtosis imaging (DKI) radiomics in differentiating clinicopathological characteristics of CC. MATERIALS AND METHODS: One hundred and seventeen histologically confirmed CC patients (mean age 56.5 ± 14.0 years) with pre-treatment magnetic resonance imaging were retrospectively reviewed. DKI was acquired with 4 b-values (0-1500 s/mm2). Volumes of interest were contoured around the tumours on T2WI and DKI. Radiomic features including shape, first-order and grey-level co-occurrence matrix with wavelet transforms were extracted. Intraclass correlation coeffient between 2 radiologists was used for features reduction. Feature selection was achieved by elastic net and minimum redundancy maximum relevance. Selected features were used to build random forest (RF) models. The performances for differentiating histological subtypes, tumour grades and FIGO stages were assessed by receiver operating characteristic analysis. RESULTS: Area under the curves (AUCs) for T2WI-only RF models for discriminating histological subtypes, tumour grades and FIGO stages were 0.762, 0.686, and 0.719. AUCs for DWI-only models were 0.663, 0.645, and 0.868, respectively. AUCs of the combined T2WI and DKI models were 0.823, 0.790, and 0.850, respectively. CONCLUSION: T2WI and DKI radiomic features could differentiate the clinicopathological characteristics of CC. A combined model showed excellent diagnostic discrimination for histological subtypes, while a DKI-only model presented the best performance in differentiating FIGO stages.

Repeatability of MR fingerprinting in normal cervix and utility in cervical carcinoma.

BACKGROUND: Magnetic resonance fingerprinting (MRF) is a fast-imaging acquisition technique that generates quantitative and co-registered parametric maps. The aim of this feasibility study was to evaluate the agreement between MRF and phantom reference values, scan-rescan repeatability of MRF in normal cervix, and its ability to distinguish cervical carcinoma (CC) from normal cervical tissues. METHODS: An International Society of Magnetic Resonance in Medicine/National Institute of Standards and Technology (ISMRM/NIST) phantom was scanned using MRF 15 times over 65 days. Agreement between MRF and phantom reference T1 and T2 values was assessed by linear regression. Healthy volunteers and patients with suspected CC were prospectively recruited. MRF was repeated twice for healthy volunteers (MRF1 and MRF2). Volumes of interest of normal cervical tissues and CC were delineated on T1 and T2 maps. MRF scan-rescan repeatability was evaluated by Bland-Altman plots, within-subject coefficients of variation (wCV), and intraclass correlation coefficients (ICC). T1 and T2 values were compared between CC and normal cervical tissues using Mann-Whitney U test. Receiver operating characteristic (ROC) analysis was performed to evaluate diagnostic efficiency. RESULTS: Strong correlations were observed between MRF and phantom (R2=0.999 for T1, 0.981 for T2). Twelve healthy volunteers (28.7±5.1 years) and 28 patients with CC (54.6±15.2 years) were recruited for the in-vivo experiments. Repeatability of MRF parameters were wCV <3% for T1, <5% for T2 and ICC ≥0.92 for T1, ≥0.94 for T2. T1 value of CC (1,529±112 ms) was higher than normal mucosa [MRF1: 1,430±129 ms, MRF2: 1,440±130 ms; P=0.031, area under the curve (AUC) ≥0.717] and normal stroma (MRF1: 1,258±101 ms, MRF2: 1,276±105 ms; P<0.001, AUC ≥0.946). T2 value of CC (69±9 ms) was lower than normal mucosa (MRF1: 88±16 ms, MRF2: 87±13 ms; P<0.001, AUC ≥0.854), but was not different from normal stroma (P=0.919). CONCLUSIONS: Excellent agreement was observed between MRF and phantom reference values. MRF exhibited excellent scan-rescan repeatability in normal cervix with potential value in differentiating CC from normal cervical tissues.

CT texture analysis in histological classification of epithelial ovarian carcinoma.

OBJECTIVES: The study aimed to compare the ability of morphological and texture features derived from contrast-enhanced CT in histological subtyping of epithelial ovarian carcinoma (EOC). METHODS: Consecutive 205 patients with newly diagnosed EOC who underwent contrast-enhanced CT were included and dichotomised into high-grade serous carcinoma (HGSC) and non-HGSC. Clinical information including age and cancer antigen 125 (CA-125) was documented. The pre-treatment images were analysed using commercial software, TexRAD, by two independent radiologists. Eight qualitative CT morphological features were evaluated, and 36 CT texture features at 6 spatial scale factors (SSFs) were extracted per patient. Features' reduction was based on kappa score, intra-class correlation coefficient (ICC), univariate ROC analysis and Pearson's correlation test. Texture features with ICC ≥ 0.8 were compared by histological subtypes. Patients were randomly divided into training and testing sets by 8:2. Two random forest classifiers were determined and compared: model 1 incorporating selected morphological and clinical features and model 2 incorporating selected texture and clinical features. RESULTS: HGSC showed specifically higher texture features than non-HGSC (p < 0.05). Both models performed highly in predicting histological subtypes of EOC (model 1: AUC 0.891 and model 2: AUC 0.937), and no statistical significance was found between the two models (p = 0.464). CONCLUSION: CT texture analysis provides objective and quantitative metrics on tumour characteristics with HGSC demonstrating specifically high texture features. The model incorporating texture analysis could classify histology subtypes of EOC with high accuracy and performed as well as morphological features. KEY POINTS: • A number of CT morphological and texture features showed good inter- and intra-observer agreements. • High-grade serous ovarian carcinoma showed specifically higher CT texture features than non-high-grade serous ovarian carcinoma. • CT texture analysis could differentiate histological subtypes of epithelial ovarian carcinoma with high accuracy.

Molecular Imaging of Peritoneal Carcinomatosis in Ovarian Carcinoma.

OBJECTIVE. This article discusses the emerging roles of 18F-FDG PET/CT and DWI in the assessment of peritoneal carcinomatosis in ovarian carcinoma from diagnostic accuracy to disease prognostication with gross pathologic correlation. CONCLUSION. PET/CT and DWI have incremental clinical values over conventional modalities with high predictive values of incomplete cytoreduction in ovarian carcinoma. The respective quantitative metrics offer evaluation of tumor burden with prognostic value in ovarian carcinoma.

Metabolic active peritoneal sites affect tumor debulking in ovarian and peritoneal cancers.

RATIONALE AND OBJECTIVES: To evaluate the impact of metabolic parameters in the peritoneal cavity on the likelihood of achieving complete tumor debulking in patients with ovarian and peritoneal cancers. MATERIALS AND METHODS: Forty-nine patients with ovarian and peritoneal cancers were included, who underwent pre-operative 18F-fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (18F-FDG PET/CT). The immediate surgical outcome was dichotomized into complete and incomplete tumor debulking. 18F-FDG PET/CT was qualitatively and quantitatively assessed by scrutinizing 15 anatomical sites for the presence of peritoneal carcinomatosis (PC). Patient-based and site-based diagnostic characteristics were described. Metabolic parameters (SUVmax, metabolic tumor volume and total lesion glycolysis) and the number of 18F-FDG avid peritoneal sites were evaluated between the two groups. Receiver operating curve (ROC) analysis was performed to determine the optimal cut-off value in predicting incomplete tumor debulking. RESULTS: Twenty-seven out of the 49 patients had PC and 11 had incomplete debulking. Patient-based and site-based accuracies for detection of PC were 87.8 and 97.6%, respectively. The number of 18F-FDG avid peritoneal sites was significantly different between complete and incomplete debulking groups (0.6 ± 0.8 versus 2.3 ± 1.7 sites respectively, p = 0.001), and the only independent significant risk factor among other metabolic parameters tested (odd ratio = 2.983, 95% CI 1.104-8.062) for incomplete tumor debulking with an optimal cut-off value of ≥4 (AUC = 0.816). CONCLUSION: The number of 18F-FDG avid peritoneal sites increased the risk of incomplete tumor debulking after surgery and potentially useful in assisting treatment stratification in patients with ovarian and peritoneal cancers.

Functional tumour burden of peritoneal carcinomatosis derived from DWI could predict incomplete tumour debulking in advanced ovarian carcinoma.

OBJECTIVES: To investigate the predictive value of peritoneal carcinomatosis (PC) quantification by DWI in determining incomplete tumour debulking in ovarian carcinoma (OC). METHODS: Prospective patients with suspected stage III-IV or recurrent OC were recruited for DWI before surgery. PC on DWI was segmented semi-automatically by k-means clustering, retaining voxels with intermediate apparent diffusion coefficient (ADC) to quantify PC burden. A scoring system, functional peritoneal cancer index (fPCI), was proposed based on the segmentation of tumour volume in 13 abdominopelvic regions with additional point given to involvement of critical sites. ADC of the largest PC was recorded. The surgical complexity and outcomes (complete vs. incomplete tumour debulking) were documented. fPCI was correlated with surgical PCI (sPCI), surgical complexity, and its ability to predict incomplete tumour debulking. RESULTS: Fifty-three patients with stage III-IV or recurrent OC were included with a mean age of 56.1 ± 11.8 years old. Complete tumour debulking was achieved in 38/53 patients (71.7%). Significant correlation was found between fPCI and sPCI (r > 0.757, p < 0.001). Patients with high-fPCI (fPCI ≥ 6) had a high surgical complexity score (p = 0.043) with 84.2% received radical or supra-radical surgery. The mean fPCI was significantly higher in patients with incomplete tumour debulking than in those with complete debulking (10.27 vs. 4.71, p < 0.001). fPCI/ADC combined with The International Federation of Gynecology and Obstetrics stage achieved 92.5% accuracy in predicting incomplete tumour debulking (AUC 0.947). CONCLUSIONS: DWI-derived fPCI offered a semi-automated estimation of PC burden. fPCI/ADC could predict the likelihood of incomplete tumour debulking with high accuracy. KEY POINTS: • Functional peritoneal cancer index (fPCI) derived from DWI offered a semi-automated estimation of tumour burden in ovarian carcinoma. • fPCI was highly correlated with surgical PCI (sPCI). • fPCI/ADC could predict the likelihood of incomplete tumour debulking with high accuracy.

MRI texture features differentiate clinicopathological characteristics of cervical carcinoma.

OBJECTIVES: To evaluate MRI texture analysis in differentiating clinicopathological characteristics of cervical carcinoma (CC). METHODS: Patients with newly diagnosed CC who underwent pre-treatment MRI were retrospectively reviewed. Texture analysis was performed using commercial software (TexRAD). Largest single-slice ROIs were manually drawn around the tumour on T2-weighted (T2W) images, apparent diffusion coefficient (ADC) maps and contrast-enhanced T1-weighted (T1c) images. First-order texture features were calculated and compared among histological subtypes, tumour grades, FIGO stages and nodal status using the Mann-Whitney U test. Feature selection was achieved by elastic net. Selected features from different sequences were used to build the multivariable support vector machine (SVM) models and the performances were assessed by ROC curves and AUC. RESULTS: Ninety-five patients with FIGO stage IB~IVB were evaluated. A number of texture features from multiple sequences were significantly different among all the clinicopathological subgroups (p < 0.05). Texture features from different sequences were selected to build the SVM models. The AUCs of SVM models for discriminating histological subtypes, tumour grades, FIGO stages and nodal status were 0.841, 0.850, 0.898 and 0.879, respectively. CONCLUSIONS: Texture features derived from multiple sequences were helpful in differentiating the clinicopathological signatures of CC. The SVM models with selected features from different sequences offered excellent diagnostic discrimination of the tumour characteristics in CC. KEY POINTS: • First-order texture features are able to differentiate clinicopathological signatures of cervical carcinoma. • Combined texture features from different sequences can offer excellent diagnostic discrimination of the tumour characteristics in cervical carcinoma.

Imaging Profile of the COVID-19 Infection: Radiologic Findings and Literature Review.

PURPOSE: To present the findings of 21 coronavirus disease 2019 (COVID-19) cases from two Chinese centers with CT and chest radiographic findings, as well as follow-up imaging in five cases. MATERIALS AND METHODS: This was a retrospective study in Shenzhen and Hong Kong. Patients with COVID-19 infection were included. A systematic review of the published literature on radiologic features of COVID-19 infection was conducted. RESULTS: The predominant imaging pattern was of ground-glass opacification with occasional consolidation in the peripheries. Pleural effusions and lymphadenopathy were absent in all cases. Patients demonstrated evolution of the ground-glass opacities into consolidation and subsequent resolution of the airspace changes. Ground-glass and consolidative opacities visible on CT are sometimes undetectable on chest radiography, suggesting that CT is a more sensitive imaging modality for investigation. The systematic review identified four other studies confirming the findings of bilateral and peripheral ground glass with or without consolidation as the predominant finding at CT chest examinations. CONCLUSION: Pulmonary manifestation of COVID-19 infection is predominantly characterized by ground-glass opacification with occasional consolidation on CT. Radiographic findings in patients presenting in Shenzhen and Hong Kong are in keeping with four previous publications from other sites.© RSNA, 2020See editorial by Kay and Abbara in this issue.

Diffusion Kurtosis Imaging in the Assessment of Cervical Carcinoma.

RATIONALE AND OBJECTIVES: To evaluate the additional value of diffusion kurtosis imaging (DKI) in the characterization of cervical carcinoma. MATERIALS AND METHODS: Seventy-five patients (56.9 ± 13.4 years) with histologic-confirmed cervical carcinoma were included. Diffusion-weighted imaging (DWI) was acquired on a 3T MRI with five b values (0, 500, 800, 1000, and 1500 s/mm2). Data were analyzed based on DKI model (5 b values) and conventional DWI (0 and 1000 s/mm2). Largest single-slice region of interest (ROI) and volume of interest (VOI) were drawn around the tumor. Mean diffusivity (MD), mean kurtosis (MK), and apparent diffusion coefficient (ADC) of cervical carcinoma and normal myometrium were measured and compared. MD, MK, and ADC of cervical carcinoma were compared among histologic subtypes, tumor grades, and FIGO stages. RESULTS: ROI- and VOI-derived DKI parameters and ADC were all in excellent consistency (intraclass correlation coefficient, ICC > 0.90, respectively). Cervical carcinoma had significantly lower MD, ADC, and higher MK than normal myometrium (p < 0.001). MD and ADC showed significant differences between histologic subtypes and FIGO stages, lower in squamous cell carcinoma than adenocarcinoma and higher in FIGO I-II than FIGO III-IV (p < 0.050), but not with tumor grade. No difference was observed in MK for different clinicopathologic features tested. CONCLUSION: ROI and VOI analyses were in excellent consistency. MD and ADC were able to distinguish histologic subtypes and separating FIGO stages, MK could not. DKI showed no clear added value over conventional DWI in the characterization of cervical carcinoma.

The Value of Contrast-Enhanced CT in the Detection of Residual Disease After Neo-Adjuvant Chemotherapy in Ovarian Cancer.

RATIONALE AND OBJECTIVES: To evaluate the diagnostic performance of contrast-enhanced computed tomography (CT) in predicting residual disease following neo-adjuvant chemotherapy (NACT) in stage III/IV ovarian cancer. MATERIALS AND METHODS: This was a retrospective observational cohort study including consecutive patients with primary stage III/IV ovarian cancer who received NACT before interval debulking surgery. CT findings before interval debulking surgerywere correlated with histological/surgical findings. Diagnostic characteristics were calculated on patient-based and lesion-based analyses. False negative results on peritoneal carcinomatosis detection were correlated with lesion size and site. RESULTS: On patient-based analysis, CT (n = 58) had a sensitivity, specificity, positive predictive value, negative predictive value and accuracy of 92.16%, 57.14%, 94.00%, 50.00%, and 87.93%. On lesion-based analysis, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 63.01%, 73.47%, 82.51%, 50.00%, and 66.51%. False negative results were associated with lesion size (p < 0.001). The diagnostic performance of CT on the detection of peritoneal carcinomatosis was low at the subdiaphragmatic spaces, bowel serosa and mesentery (p < 0.001). CONCLUSION: CT had low negative predictive value in determining residual disease following NACT on both patient-based and lesion-based analyses, especially for non-measurable lesions and at the subdiaphragmatic spaces, bowel serosa and mesentery.

Concordance of FDG PET/CT metabolic tumour volume versus DW-MRI functional tumour volume with T2-weighted anatomical tumour volume in cervical cancer.

BACKGROUND: 18F-fluoro-deoxyglucose positron emission tomography with computed tomography (FDG PET/CT) has been employed to define radiotherapy targets using a threshold based on the standardised uptake value (SUV), and has been described for use in cervical cancer. The aim of this study was to evaluate the concordance between the metabolic tumour volume (MTV) measured on FDG PET/CT and the anatomical tumour volume (ATV) measured on T2-weighted magnetic resonance imaging (T2W-MRI); and compared with the functional tumour volume (FTV) measured on diffusion-weighted MRI (DW-MRI) in cervical cancer, taking the T2W-ATV as gold standard. METHODS: Consecutive newly diagnosed cervical cancer patients who underwent FDG PET/CT and DW-MRI were retrospectively reviewed from June 2013 to July 2017. Volumes of interest was inserted to the focal hypermetabolic activity corresponding to the cervical tumour on FDG PET/CT with automated tumour contouring and manual adjustment, based on SUV 20%-80% thresholds of the maximum SUV (SUVmax) to define the MTV20-80, with intervals of 5%. Tumour areas were manually delineated on T2W-MRI and multiplied by slice thickness to calculate the ATV. FTV were derived by manually delineating tumour area on ADC map, multiplied by the slice thickness to determine the FTV(manual). Diffusion restricted areas was extracted from b0 and ADC map using K-means clustering to determine the FTV(semi-automated). The ATVs, FTVs and the MTVs at different thresholds were compared using the mean and correlated using Pearson's product-moment correlation. RESULTS: Twenty-nine patients were evaluated (median age 52 years). Paired difference of mean between ATV and MTV was the closest and not statistically significant at MTV30 (-2.9cm3, -5.2%, p = 0.301). This was less than the differences between ATV and FTV(semi-automated) (25.0cm3, 45.1%, p < 0.001) and FTV(manual) (11.2cm3, 20.1%, p = 0.001). The correlation of MTV30 with ATV was excellent (r = 0.968, p < 0.001) and better than that of the FTVs. CONCLUSIONS: Our study demonstrated that MTV30 was the only parameter investigated with no statistically significant difference with ATV, had the least absolute difference from ATV, and showed excellent positive correlation with ATV, suggesting its superiority as a functional imaging modality when compared with DW-MRI and supporting its use as a surrogate for ATV for radiotherapy tumour contouring.

Assessment of Cervical Cancer with a Parameter-Free Intravoxel Incoherent Motion Imaging Algorithm.

OBJECTIVE: To evaluate the feasibility of a parameter-free intravoxel incoherent motion (IVIM) approach in cervical cancer, to assess the optimal b-value threshold, and to preliminarily examine differences in the derived perfusion and diffusion parameters for different histological cancer types. MATERIALS AND METHODS: After Institutional Review Board approval, 19 female patients (mean age, 54 years; age range, 37-78 years) gave consent and were enrolled in this prospective magnetic resonance imaging study. Clinical staging and biopsy results were obtained. Echo-planar diffusion weighted sequences at 13 b-values were acquired at 3 tesla field strength. Single-sliced region-of-interest IVIM analysis with adaptive b-value thresholds was applied to each tumor, yielding the optimal fit and the optimal parameters for pseudodiffusion (D*), perfusion fraction (Fp) and diffusion coefficient (D). Monoexponential apparent diffusion coefficient (ADC) was calculated for comparison with D. RESULTS: Biopsy revealed squamous cell carcinoma in 10 patients and adenocarcinoma in 9. The b-value threshold (median [interquartile range]) depended on the histological type and was 35 (22.5-50) s/mm2 in squamous cell carcinoma and 150 (100-150) s/mm2 in adenocarcinoma (p < 0.05). Comparing squamous cell vs. adenocarcinoma, D* (45.1 [25.1-60.4] × 10-3 mm2/s vs. 12.4 [10.5-21.2] × 10-3 mm2/s) and Fp (7.5% [7.0-9.0%] vs. 9.9% [9.0-11.4%]) differed significantly between the subtypes (p < 0.02), whereas D did not (0.89 [0.75-0.94] × 10-3 mm2/s vs. 0.90 [0.82-0.97] × 10-3 mm2/s, p = 0.27). The residuals did not differ (0.74 [0.60-0.92] vs. 0.94 [0.67-1.01], p = 0.32). The ADC systematically underestimated the magnitude of diffusion restriction compared to D (p < 0.001). CONCLUSION: The parameter-free IVIM approach is feasible in cervical cancer. The b-value threshold and perfusion-related parameters depend on the tumor histology type.

Dual-Tracer PET/CT Using 18F-FDG and 11C-Acetate in Gastric Adenocarcinoma With Liver Metastasis.

Dual-tracer F-FDG and C-acetate PET/CT has been shown to demonstrate good sensitivity and specificity for the diagnosis of hepatocellular carcinoma. We present a case of gastric adenocarcinoma with liver metastasis with positive uptake of F-FDG and C-acetate highlighting an unusual appearance in dual-tracer PET/CT.