Xinming Zhao

Comparison of 68Ga-FAPI-04 and 18F-FDG PET/CT in diagnosing ovarian cancer

This study assesses the diagnostic performance of Seventy-nine ovarian cancer patients who performed Among all patients, 6 were assessed for initial assessment and 73 for recurrence and metastasis detection. For all lesions,

Unusual extramedullary presentation in newly diagnosed multiple myeloma: Synchronous infiltration of the maxillary sinus, thyroid, breast and bilateral ovaries

Feasibility of One-Day PET/CT Scanning Protocol with 68 Ga-DOTA-FAPI-04 and 18 F-FDG for the Detection of Ovarian Cancer Recurrence and Metastasis

Objective: The objective of this study was to investigate the feasibility of 1-d 68 Ga-DOTA-FAPI-04 and 18 F-FDG (2-deoxy-2[ 18 F]fluoro- d -glucose) positron emission tomography/computed tomography (PET/CT) for detecting ovarian cancer recurrence and metastasis. Materials and Methods: Fifty-two patients who underwent 18 F-FDG and 68 Ga-DOTA-FAPI-04 PET/CT were divided into 1- and 2-d groups. Image acquisition, injection time, and total waiting time were compared. For the 68 Ga-DOTA-FAPI-04 PET/CT scans, low-dose CT scans and low injection dosages were employed, and total radiation dose was assessed for both protocols. The comparative analysis included assessment of patient-based detection rates and lesion-based diagnostic efficacy. Results: The total waiting time was significantly shorter in the 1-d group than in the 2-d group ( p  = 0.000). The radiation doses stemming from internal radiation and external radiation between the groups showed no differences ( p  = 0.151 vs. 0.716). In the patient-based analysis, the detection rates for local recurrence, peritoneal, lymph node, and other metastases were not significantly different in both protocols ( p ∈ [0.351, 1.000]). For the lesion-based analysis, no differences were noted in terms of sensitivity, specificity, positive predictive value, negative predictive value, and accuracy ( p ∈ [0.371, 1.000]). Conclusions: The 1-d PET/CT protocol reduced waiting time and exhibited equivalent detectability compared with the 2-d protocol, suggesting its clinical value.

Added-value of dynamic contrast-enhanced MRI on prediction of tumor recurrence in locally advanced cervical cancer treated with chemoradiotherapy

To evaluate whether the DCE-MRI derived parameters integrated into clinical and conventional imaging variables may improve the prediction of tumor recurrence for locally advanced cervical cancer (LACC) patients following concurrent chemoradiotherapy (CCRT). Between March 2014 and November 2019, 79 consecutive LACC patients who underwent pelvic MRI examinations with DCE-MRI sequence before treatment were prospectively enrolled. The primary outcome was disease-free survival (DFS). DCE-MRI derived parameters, conventional imaging, and clinical factors were collected. Univariate and multivariate Cox hazard regression analyses were performed to evaluate these parameters in the prediction of DFS. The independent and prognostic interested variables were combined to build a prediction model compared with the clinical International Federation of Gynecological (FIGO) staging system. Lymph node metastasis (LNM) and the mean value of v The v Lower v

Whole-tumor texture model based on diffusion kurtosis imaging for assessing cervical cancer: a preliminary study

To evaluate the diagnostic potential of diffusion kurtosis imaging (DKI) functional maps with whole-tumor texture analysis in differentiating cervical cancer (CC) subtype and grade. Seventy-six patients with CC were enrolled. First-order texture features of the whole tumor were extracted from DKI and DWI functional maps, including apparent kurtosis coefficient averaged over all directions (MK), kurtosis along the axial direction (Ka), kurtosis along the radial direction (Kr), mean diffusivity (MD), fractional anisotropy (FA), and ADC maps, respectively. The Mann-Whitney U test and ROC curve were used to select the most representative texture features. Models based on each individual and combined functional maps were established using multivariate logistic regression analysis. Conventional parameters-the average values of ADC and DKI parameters derived from the conventional ROI method-were also evaluated. The combined model based on Ka, Kr, MD, and FA maps yielded the best diagnostic performance in discrimination of cervical squamous cell cancer (SCC) and cervical adenocarcinoma (CAC) with the highest AUC (0.932). Among individual functional map derived models, Kr map-derived model showed the best performance when differentiating tumor subtypes (AUC = 0.828). MK_90th percentile was useful for distinguishing high-grade and low-grade in SCC tumors with an AUC of 0.701. The average values of MD, FA, and ADC were significantly different between SCC and CAC, but no conventional parameters were useful for tumor grading. The whole-tumor texture analysis applied to DKI functional maps can be used for differential diagnosis of cervical cancer subtypes and grading SCC. • The whole-tumor texture analysis applied to DKI functional maps allows accurate differential diagnosis of CC subtype and grade. • The combined model derived from multiple functional maps performs significantly better than the single models when differentiating tumor subtypes. • MK_90th percentile was useful for distinguishing poorly and well-/moderately differentiated SCC tumors with an AUC of 0.701.

MRI-based radiomics value for predicting the survival of patients with locally advanced cervical squamous cell cancer treated with concurrent chemoradiotherapy

Abstract Background To investigate the magnetic resonance imaging (MRI)-based radiomics value in predicting the survival of patients with locally advanced cervical squamous cell cancer (LACSC) treated with concurrent chemoradiotherapy (CCRT). Methods A total of 185 patients (training group: n = 128; testing group: n = 57) with LACSC treated with CCRT between January 2014 and December 2018 were retrospectively enrolled in this study. A total of 400 radiomics features were extracted from T2-weighted imaging, apparent diffusion coefficient map, arterial- and delayed-phase contrast-enhanced MRI. Univariate Cox regression and least absolute shrinkage and selection operator Cox regression was applied to select radiomics features and clinical characteristics that could independently predict progression-free survival (PFS) and overall survival (OS). The predictive capability of the prediction model was evaluated using Harrell’s C-index. Nomograms and calibration curves were then generated. Survival curves were generated using the Kaplan-Meier method, and the log-rank test was used for comparison. Results The radiomics score achieved significantly better predictive performance for the estimation of PFS (C-index, 0.764 for training and 0.762 for testing) and OS (C-index, 0.793 for training and 0.750 for testing), compared with the 2018 FIGO staging system (C-index for PFS, 0.657 for training and 0.677 for testing; C-index for OS, 0.665 for training and 0.633 for testing) and clinical-predicting model (C-index for PFS, 0.731 for training and 0.725 for testing; C-index for OS, 0.708 for training and 0.693 for testing) (P < 0.05). The combined model constructed with T stage, lymph node metastasis position, and radiomics score achieved the best performance for the estimation of PFS (C-index, 0.792 for training and 0.809 for testing) and OS (C-index, 0.822 for training and 0.785 for testing), which were significantly higher than those of the radiomics score (P < 0.05). Conclusions The MRI-based radiomics score could provide effective information in predicting the PFS and OS in patients with LACSC treated with CCRT. The combined model (including MRI-based radiomics score and clinical characteristics) showed the best prediction performance.