Toripalimab plus capecitabine in the treatment of patients with residual nasopharyngeal carcinoma: a single-arm phase 2 trial.

Patients with residual nasopharyngeal carcinoma after receiving definitive treatment have poor prognoses. Although immune checkpoint therapies have achieved breakthroughs for treating recurrent and metastatic nasopharyngeal carcinoma, none of these strategies have been assessed for treating residual nasopharyngeal carcinoma. In this single-arm, phase 2 trial, we aimed to evaluate the antitumor efficacy and safety of toripalimab (anti-PD1 antibody) plus capecitabine in patients with residual nasopharyngeal carcinoma after definitive treatment (ChiCTR1900023710). Primary endpoint of this trial was the objective response rate assessed according to RECIST (version 1.1). Secondary endpoints included complete response rate, disease control rate, duration of response, progression-free survival, safety profile, and treatment compliance. Between June 1, 2020, and May 31, 2021, 23 patients were recruited and received six cycles of toripalimab plus capecitabine every 3 weeks. In efficacy analyses, 13 patients (56.5%) had complete response, and 9 patients (39.1%) had partial response, with an objective response rate of 95.7% (95% CI 78.1-99.9). The trial met its prespecified primary endpoint. In safety analyses, 21 of (91.3%) 23 patients had treatment-related adverse events. The most frequently reported adverse event was hand-foot syndrome (11 patients [47.8%]). The most common grade 3 adverse event was hand-foot syndrome (two patients [8.7%]). No grades 4-5 treatment-related adverse events were recorded. This phase 2 trial shows that combining toripalimab with capecitabine has promising antitumour activity and a manageable safety profile for patients with residual nasopharyngeal carcinoma.

A deep-learning approach for segmentation of liver tumors in magnetic resonance imaging using UNet+.

OBJECTIVE: Radiomic and deep learning studies based on magnetic resonance imaging (MRI) of liver tumor are gradually increasing. Manual segmentation of normal hepatic tissue and tumor exhibits limitations. METHODS: 105 patients diagnosed with hepatocellular carcinoma were retrospectively studied between Jan 2015 and Dec 2020. The patients were divided into three sets: training (n = 83), validation (n = 11), and internal testing (n = 11). Additionally, 9 cases were included from the Cancer Imaging Archive as the external test set. Using the arterial phase and T2WI sequences, expert radiologists manually delineated all images. Using deep learning, liver tumors and liver segments were automatically segmented. A preliminary liver segmentation was performed using the UNet + + network, and the segmented liver mask was re-input as the input end into the UNet + + network to segment liver tumors. The false positivity rate was reduced using a threshold value in the liver tumor segmentation. To evaluate the segmentation results, we calculated the Dice similarity coefficient (DSC), average false positivity rate (AFPR), and delineation time. RESULTS: The average DSC of the liver in the validation and internal testing sets was 0.91 and 0.92, respectively. In the validation set, manual and automatic delineation took 182.9 and 2.2 s, respectively. On an average, manual and automatic delineation took 169.8 and 1.7 s, respectively. The average DSC of liver tumors was 0.612 and 0.687 in the validation and internal testing sets, respectively. The average time for manual and automatic delineation and AFPR in the internal testing set were 47.4 s, 2.9 s, and 1.4, respectively, and those in the external test set were 29.5 s, 4.2 s, and 1.6, respectively. CONCLUSION: UNet + + can automatically segment normal hepatic tissue and liver tumors based on MR images. It provides a methodological basis for the automated segmentation of liver tumors, improves the delineation efficiency, and meets the requirement of extraction set analysis of further radiomics and deep learning.

Pretreatment MRI-Based Radiomics for Prediction of Rectal Cancer Outcome: A Discovery and Validation Study.

RATIONALE AND OBJECTIVES: Accurate prediction of local recurrence or distant metastasis is critical for developing individualized therapies for locally advanced rectal cancer (LARC) patients after standard therapy. This study aims to develop and validate a multiparameter MRI-based radiomics signature (RS) for prognostic prediction in LARC patients receiving neoadjuvant chemoradiotherapy (nCRT) and total mesorectal excision (TME) and to explore the ability of RS for personalized survival risk stratification. MATERIALS AND METHODS: In this multi-center study, 454 patients who received nCRT and TME and completed 3 years of follow-up participated. RS was constructed for prognostic prediction based on features extracted from pretreatment multiparameter MRI in a training cohort (TC; n = 298), which was tested in an internal validation cohort (IVC; n = 75) and further validated in an independent external validation cohort (EVC; n = 81). Furthermore, the ability of RS for personalized survival risk stratification was explored using the Kaplan-Meier survival curves. RESULTS: The RS model showed satisfactory accuracy for prognostic prediction with AUCs of 0.83, 0.81 and 0.82 in the TC, IVC and EVC, respectively. In addition, RS helped to refine risk stratification for LARC patients on the basis of significantly different 3-year disease-free survival rates, independent of their pathological stage, pre-surgery CEA, and even treatment modality. CONCLUSIONS: The proposed RS can be used not only to predict local recurrence or distant metastasis but also to serve as an effective postoperative survival risk stratification tool for clinicians to facilitate decision-making for LARC patients receiving standard treatment.

Multiphase and multiparameter MRI-based radiomics for prediction of tumor response to neoadjuvant therapy in locally advanced rectal cancer.

BACKGROUND: To develop and validate radiomics models for prediction of tumor response to neoadjuvant therapy (NAT) in patients with locally advanced rectal cancer (LARC) using both pre-NAT and post-NAT multiparameter magnetic resonance imaging (mpMRI). METHODS: In this multicenter study, a total of 563 patients were included from two independent centers. 453 patients from center 1 were split into training and testing cohorts, the remaining 110 from center 2 served as an external validation cohort. Pre-NAT and post-NAT mpMRI was collected for feature extraction. The radiomics models were constructed using machine learning from a training cohort. The accuracy of the models was verified in a testing cohort and an independent external validation cohort. Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS: The model constructed with pre-NAT mpMRI had favorable accuracy for prediction of non-response to NAT in the training cohort (AUC = 0.84), testing cohort (AUC = 0.81), and external validation cohort (AUC = 0.79). The model constructed with both pre-NAT and post-NAT mpMRI had powerful diagnostic value for pathologic complete response in the training cohort (AUC = 0.86), testing cohort (AUC = 0.87), and external validation cohort (AUC = 0.87). CONCLUSIONS: Models constructed with multiphase and multiparameter MRI were able to predict tumor response to NAT with high accuracy and robustness, which may assist in individualized management of LARC.

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.

Radiomics Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Preoperative Differentiation of Combined Hepatocellular-Cholangiocarcinoma from Hepatocellular Carcinoma: A Multi-Center Study.

Purpose: To explore whether texture features based on magnetic resonance can distinguish diseases combined hepatocellular-cholangiocarcinoma (cHCC-CC) from hepatocellular carcinoma (HCC) before operation. Methods: The clinical baseline data and MRI information of 342 patients with pathologically diagnosed cHCC-CC and HCC in two medical centers were collected. The data were divided into the training set and the test set at a ratio of 7:3. MRI images of tumors were segmented with ITK-SNAP software, and python open-source platform was used for texture analysis. Logistic regression as the base model, mutual information (MI) and Least Absolute Shrinkage and Selection Operator (LASSO) regression were used to select the most favorable features. The clinical, radiomics, and clinic-radiomics model were constructed based on logistic regression. The model's effectiveness was comprehensively evaluated by the receiver operating characteristic (ROC) curve, area under the curve (AUC), sensitivity, specificity, and Youden index which is the main, and the model results were exported by SHapley Additive exPlanations (SHAP). Results: A total of 23 features were included. Among all models, the arterial phase-based clinic-radiomics model showed the best performance in differentiating cHCC-CC from HCC before an operation, with the AUC of the test set being 0.863 (95% CI: 0.782 to 0.923), the specificity and sensitivity being 0.918 (95% CI: 0.819 to 0.973) and 0.738 (95% CI: 0.580 to 0.861), respectively. SHAP value results showed that the RMS was the most important feature affecting the model. Conclusion: Clinic-radiomics model based on DCE-MRI may be useful to distinguish cHCC-CC from HCC in a preoperative setting, especially in the arterial phase, and RMS has the greatest impact.

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.

Prediction of Individual Lymph Node Metastatic Status in Esophageal Squamous Cell Carcinoma Using Routine Computed Tomography Imaging: Comparison of Size-Based Measurements and Radiomics-Based Models.

BACKGROUND: Lymph node status is vital for prognosis and treatment decisions for esophageal squamous cell carcinoma (ESCC). This study aimed to construct and evaluate an optimal radiomics-based method for a more accurate evaluation of individual regional lymph node status in ESCC and to compare it with traditional size-based measurements. METHODS: The study consecutively collected 3225 regional lymph nodes from 530 ESCC patients receiving upfront surgery from January 2011 to October 2015. Computed tomography (CT) scans for individual lymph nodes were analyzed. The study evaluated the predictive performance of machine-learning models trained on features extracted from two-dimensional (2D) and three-dimensional (3D) radiomics by different contouring methods. Robust and important radiomics features were selected, and classification models were further established and validated. RESULTS: The lymph node metastasis rate was 13.2% (427/3225). The average short-axis diameter was 6.4 mm for benign lymph nodes and 7.9 mm for metastatic lymph nodes. The division of lymph node stations into five regions according to anatomic lymph node drainage (cervical, upper mediastinal, middle mediastinal, lower mediastinal, and abdominal regions) improved the predictive performance. The 2D radiomics method showed optimal diagnostic results, with more efficient segmentation of nodal lesions. In the test set, this optimal model achieved an area under the receiver operating characteristic curve of 0.841-0.891, an accuracy of 84.2-94.7%, a sensitivity of 65.7-83.3%, and a specificity of 84.4-96.7%. CONCLUSIONS: The 2D radiomics-based models noninvasively predicted the metastatic status of an individual lymph node in ESCC and outperformed the conventional size-based measurement. The 2D radiomics-based model could be incorporated into the current clinical workflow to enable better decision-making for treatment strategies.

Radiomics Based on Contrast-Enhanced MRI in Differentiation Between Fat-Poor Angiomyolipoma and Hepatocellular Carcinoma in Noncirrhotic Liver: A Multicenter Analysis.

OBJECTIVE: This study aims to develop and externally validate a contrast-enhanced magnetic resonance imaging (CE-MRI) radiomics-based model for preoperative differentiation between fat-poor angiomyolipoma (fp-AML) and hepatocellular carcinoma (HCC) in patients with noncirrhotic livers and to compare the diagnostic performance with that of two radiologists. METHODS: This retrospective study was performed with 165 patients with noncirrhotic livers from three medical centers. The dataset was divided into a training cohort (n = 99), a time-independent internal validation cohort (n = 24) from one center, and an external validation cohort (n = 42) from the remaining two centers. The volumes of interest were contoured on the arterial phase (AP) images and then registered to the venous phase (VP) and delayed phase (DP), and a total of 3,396 radiomics features were extracted from the three phases. After the joint mutual information maximization feature selection procedure, four radiomics logistic regression classifiers, including the AP model, VP model, DP model, and combined model, were built. The area under the receiver operating characteristic curve (AUC), diagnostic accuracy, sensitivity, and specificity of each radiomics model and those of two radiologists were evaluated and compared. RESULTS: The AUCs of the combined model reached 0.789 (95%CI, 0.579-0.999) in the internal validation cohort and 0.730 (95%CI, 0.563-0.896) in the external validation cohort, higher than the AP model (AUCs, 0.711 and 0.638) and significantly higher than the VP model (AUCs, 0.594 and 0.610) and the DP model (AUCs, 0.547 and 0.538). The diagnostic accuracy, sensitivity, and specificity of the combined model were 0.708, 0.625, and 0.750 in the internal validation cohort and 0.619, 0.786, and 0.536 in the external validation cohort, respectively. The AUCs for the two radiologists were 0.656 and 0.594 in the internal validation cohort and 0.643 and 0.500 in the external validation cohort. The AUCs of the combined model surpassed those of the two radiologists and were significantly higher than that of the junior one in both validation cohorts. CONCLUSIONS: The proposed radiomics model based on triple-phase CE-MRI images was proven to be useful for differentiating between fp-AML and HCC and yielded comparable or better performance than two radiologists in different centers, with different scanners and different scanning parameters.

Divergent effects of irradiation on brain cortical morphology in patients with nasopharyngeal carcinoma: one-year follow-up study using structural magnetic resonance imaging.

BACKGROUND: Increasing evidence indicates that radiotherapy (RT)-induced brain cortical deficits may play a critical role in developing radiation encephalopathy in patients with nasopharyngeal carcinoma (NPC). However, the evolutional processes of RT-induced cortical injury have not been sufficiently investigated. This study investigates RT-induced effects on cortical morphology using longitudinal structural magnetic resonance imaging (MRI) in NPC patients. METHODS: Using MRI-based morphometry with surface-based measures, we evaluated the longitudinal alterations of cortical volume (CV), cortical thickness (CT), and cortical surface area (CSA) in 104 NPC patients at pre-RT (n=104), within 3 months post-RT (n=92), 6 months post-RT (n=71), and 9-12 months post-RT (n=52). Twenty healthy controls were also evaluated in parallel. Linear mixed models were used to investigate the trajectories of RT-related changes in cortical brain morphology and its association with irradiation dose, with healthy controls data being used to construct a normal age-related benchmark. The level of statistical significance was set at P<0.05, corrected for multiple comparisons. RESULTS: The results showed that RT-related longitudinal alterations in cortical morphology underwent two diverse patterns during the first year of follow up in NPC patients. The temporal cortices (including the bilateral superior temporal gyrus, middle temporal gyrus, temporal pole, parahippocampal and fusiform gyrus, and the right inferior temporal and right transverse temporal gyrus), the basal occipital cortices (the right lingual gyrus and lateral occipital gyrus), and the basal frontal cortices (the right lateral orbitofrontal gyrus) showed time-dependent attenuation in cortical morphology indices. Furthermore, these effects on multiple cortices were dose-dependent, suggesting they were RT-associated. In contrast, in the left rostral middle frontal gyrus, there was a time-dependent increase in CT. CONCLUSIONS: Our preliminary findings revealed divergent effects of irradiation on cortical brain morphology. These results contribute to a more comprehensive understanding of the underlying neural mechanisms of irradiation-related neurotoxic effects on cortical brain morphology and will help guide the investigation of critically neuroprotective strategies.

Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma-A Pilot Study.

PURPOSE: To evaluate the prognostic value of baseline and restaging CT-based radiomics with features associated with gene expression in esophageal squamous cell carcinoma (ESCC) patients receiving neoadjuvant chemoradiation (nCRT) plus surgery. METHODS: We enrolled 106 ESCC patients receiving nCRT from two institutions. Gene expression profiles of 28 patients in the training set were used to detect differentially expressed (DE) genes between patients with and without relapse. Radiomic features that were correlated to DE genes were selected, followed by additional machine learning selection. A radiomic nomogram for disease-free survival (DFS) prediction incorporating the radiomic signature and prognostic clinical characteristics was established for DFS estimation and validated. RESULTS: The radiomic signature with DE genes feature selection achieved better performance for DFS prediction than without. The nomogram incorporating the radiomic signature and lymph nodal status significantly stratified patients into high and low-risk groups for DFS (p < 0.001). The areas under the curve (AUCs) for predicting 5-year DFS were 0.912 in the training set, 0.852 in the internal test set, 0.769 in the external test set. CONCLUSIONS: Genomics association was useful for radiomic feature selection. The established radiomic signature was prognostic for DFS. The radiomic nomogram could provide a valuable prediction for individualized long-term survival.

Standard radiotherapy for patients with nasopharyngeal carcinoma results in progressive tract-specific brain white matter alterations: A one-year follow-up via diffusion tensor imaging.

BACKGROUND AND PURPOSE: Radiation therapy (RT)-induced neurocognitive disability may be mediated by brain tissue damage. The aim of the present study was to investigate the effects of standard RT on normal brain tissue via in vivo neuroimaging in patients with nasopharyngeal carcinoma (NPC). MATERIALS AND METHODS: A total of 146 newly diagnosed NPC patients who were treated with standard RT were longitudinally followed up at multiple time points during the first year post-RT, with 19 comparable healthy controls followed up in parallel serving as normal age-related benchmarks. Magnetic resonance diffusion tensor imaging was used to evaluate longitudinal brain white matter tract changes in NPC patients. The relationships between RT-related white matter changes, hippocampal atrophy, and cognitive impairment were also assessed. RESULTS: Bilateral cingulate angular bundle (CAB) fibers had progressive diffusion reduction [radial diffusivity (RD) and mean diffusivity] over time (P < 0.05, corrected for multiple comparisons) in NPC patients during the first year after RT. RT-associated progressive RD reduction in the left CAB correlated with longitudinal atrophy of the ipsilateral hippocampus (P = 0.033). Additionally, RT-associated progressive RD reduction in the left CAB correlated with progressive cognitive impairment in NPC patients post-RT (P = 0.048). CONCLUSION: We present evidence of progressive RT-associated changes in the bilateral CAB in NPC patients, which may underlie RT-related cognitive impairment. These findings illustrate that the use of white matter tract alterations as potential biomarkers to detect RT-related brain injury in NPC patients may be useful for better understanding the pathogenesis of RT-induced cognitive decline.

Inhibition of galectin-3 augments the antitumor efficacy of PD-L1 blockade in non-small-cell lung cancer.

Multiple clinical trials have shown that monoclonal antibodies (mAbs) against programmed death-ligand 1 (PD-1/PD-L1) can benefit patients with lung cancer by increasing their progression-free survival and overall survival. However, a significant proportion of patients do not respond to anti-PD-1/PD-L1 mAbs. In the present study, we investigated whether galectin (Gal)-3 inhibitors can enhance the antitumor effect of PD-L1 blockade. Using the NSCLC-derived cell line A549, we examined the expression of Gal-3 in lung cancer cells under hypoxic conditions and investigated the regulatory effect of Gal-3 on PD-L1 expression, which is mediated by the STAT3 pathway. We also explored whether Gal-3 inhibition can facilitate the cytotoxic effect of T cells induced by PD-L1 blockade. The effects of combined use of a Gal-3 inhibitor and PD-L1 blockade on tumor growth and T-cell function were also investigated, and we found that hypoxia increased the expression and secretion of Gal-3 by lung cancer cells. Gal-3 increased PD-L1 expression via the upregulation of STAT3 phosphorylation, and administration of a Gal-3 inhibitor enhanced the effect of PD-L1 blockade on the cytotoxic activity of T cells against cancer cells in vitro. In a mouse xenograft model, the combination of a Gal-3 inhibitor and PD-L1 blockade synergistically suppressed tumor growth. Furthermore, the administration of a Gal-3 inhibitor enhanced T-cell infiltration and granzyme B release in tumors. Collectively, our results show that Gal-3 increases PD-L1 expression in lung cancer cells and that the administration of a Gal-3 inhibitor as an adjuvant enhanced the antitumor activity of PD-L1 blockade.

Nomogram for the prediction of primary distant metastasis of nasopharyngeal carcinoma to guide individualized application of FDG PET/CT.

PURPOSE: This study aimed to establish an effective nomogram to predict primary distant metastasis (DM) in patients with nasopharyngeal carcinoma (NPC) to guide the application of PET/CT. METHODS: In total, 3591 patients with pathologically confirmed NPC were consecutively enrolled. The nomogram was constructed based on 1922 patients treated between 2007 and 2014. Multivariate logistical regression was applied to identify the independent risk factors of DM. The predictive value of the nomogram was evaluated using the concordance index (C-index), calibration curve, probability density functions (PDFs), and clinical utility curve (CUC). The results were validated in 1669 patients enrolled from 2015 to 2016. Net reclassification improvement (NRI) was applied to compare performances of the nomogram with other clinical factors. The best cut-off value of the nomogram chosen for clinical application was analyzed. RESULTS: A total of 355 patients showed primary DM among 3591 patients, yielding an incidence rate of 9.9%. Sex, N stage, EBV DNA level, lactate dehydrogenase level, and hemoglobin level were independent predictive factors for primary DM. C-indices in the training and validation cohort were 0.796 (95% CI, 0.76-0.83) and 0.779 (95% CI, 0.74-0.81), respectively. The NRI indices demonstrated that this model had better predictive performance than plasma EBV DNA level and N stage. We advocate for a threshold probability of 3.5% for guiding the application of PET/CT depending on the clinical utility analyses. CONCLUSION: This nomogram is a useful tool to predict primary DM of NPC and guide the clinical application of PET/CT individually at the initial staging.

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.

Low value of whole-body dual-modality [18f]fluorodeoxyglucose positron emission tomography/computed tomography in primary staging of stage I-II nasopharyngeal carcinoma: a nest case-control study.

OBJECTIVES: The value of using PET/CT for staging of stage I-II NPC remains unclear. Hence, we aimed to investigate the survival benefit of PET/CT for staging of early-stage NPC before radical therapy. METHODS: A total of 1003 patients with pathologically confirmed NPC of stages I-II were consecutively enrolled. Among them, 218 patients underwent both PET/CT and conventional workup ([CWU], head-and-neck MRI, chest radiograph, liver ultrasound, bone scintigraphy) before treatment. The remaining 785 patients only underwent CWU. The standard of truth (SOT) for lymph node metastasis was defined by the change of size according to follow-up MRI. The diagnostic efficacies were compared in 218 patients who underwent both PET/CT and CWU. After covariate adjustment using propensity scoring, a cohort of 872 patients (218 with and 654 without pre-treatment PET/CT) was included. The primary outcome was overall survival based on intention to treat. RESULTS: Retropharyngeal lymph nodes were metastatic based on follow-up MRI in 79 cases. PET/CT was significantly less sensitive than MRI in detecting retropharyngeal lymph node lesions (72.2% [62.3-82.1] vs. 91.1% [84.8-97.4], p = 0.004). Neck lymph nodes were metastatic in 89 cases and PET/CT was more sensitive than MRI (96.6% [92.8-100.0] vs. 76.4% [67.6-85.2], p < 0.001). In the survival analyses, there was no association between pre-treatment PET/CT use and improved overall survival, progression-free survival, local relapse-free survival, regional relapse-free survival, and distant metastasis-free survival. CONCLUSIONS: This study showed PET/CT is of little value for staging of stage I-II NPC patients at initial imaging. KEY POINTS: • PET/CT was more sensitive than MRI in detecting neck lymph node lesions whereas it was significantly less sensitive than MRI in detecting retropharyngeal lymph node lesions. • No association existed between pre-treatment PET/CT use and improved survival in stage I-II NPC patients.

Combining quantitative and qualitative magnetic resonance imaging features to differentiate anorectal malignant melanoma from low rectal cancer.

Background: Distinguishing anorectal malignant melanoma from low rectal cancer remains challenging because of the overlap of clinical symptoms and imaging findings. We aim to investigate whether combining quantitative and qualitative magnetic resonance imaging (MRI) features could differentiate anorectal malignant melanoma from low rectal cancer. Methods: Thirty-seven anorectal malignant melanoma and 98 low rectal cancer patients who underwent pre-operative rectal MRI from three hospitals were retrospectively enrolled. All patients were divided into the primary cohort (N = 84) and validation cohort (N = 51). Quantitative image analysis was performed on T1-weighted (T1WI), T2-weighted (T2WI), and contrast-enhanced T1-weighted imaging (CE-T1WI). The subjective qualitative MRI findings were evaluated by two radiologists in consensus. Multivariable analysis was performed using stepwise logistic regression. The discrimination performance was assessed by the area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI). Results: The skewness derived from T2WI (T2WI-skewness) showed the best discrimination performance among the entire quantitative image features for differentiating anorectal malignant melanoma from low rectal cancer (primary cohort: AUC = 0.852, 95% CI 0.788-0.916; validation cohort: 0.730, 0.645-0.815). Multivariable analysis indicated that T2WI-skewness and the signal intensity of T1WI were independent factors, and incorporating both factors achieved good discrimination performance in two cohorts (primary cohort: AUC = 0.913, 95% CI 0.868-0.958; validation cohort: 0.902, 0.844-0.960). Conclusions: Incorporating T2WI-skewness and the signal intensity of T1WI achieved good performance for differentiating anorectal malignant melanoma from low rectal cancer. The quantitative image analysis helps improve diagnostic accuracy.

Computed tomography-based deep-learning prediction of neoadjuvant chemoradiotherapy treatment response in esophageal squamous cell carcinoma.

BACKGROUND: Deep learning is promising to predict treatment response. We aimed to evaluate and validate the predictive performance of the CT-based model using deep learning features for predicting pathologic complete response to neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC). MATERIALS AND METHODS: Patients were retrospectively enrolled between April 2007 and December 2018 from two institutions. We extracted deep learning features of six pre-trained convolutional neural networks, respectively, from pretreatment CT images in the training cohort (n = 161). Support vector machine was adopted as the classifier. Validation was performed in an external testing cohort (n = 70). We assessed the performance using the area under the receiver operating characteristics curve (AUC) and selected an optimal model, which was compared with a radiomics model developed from the training cohort. A clinical model consisting of clinical factors only was also built for baseline comparison. We further conducted a radiogenomics analysis using gene expression profiles to reveal underlying biology associated with radiological prediction. RESULTS: The optimal model with features extracted from ResNet50 achieved an AUC and accuracy of 0.805 (95% CI, 0.696-0.913) and 77.1% (65.6%-86.3%) in the testing cohort, compared with 0.725 (0.605-0.846)) and 67.1% (54.9%-77.9%) for the radiomics model. All the radiological models showed better predictive performance than the clinical model. Radiogenomics analysis suggested a potential association mainly with WNT signaling pathway and tumor microenvironment. CONCLUSIONS: The novel and noninvasive deep learning approach could provide efficient and accurate prediction of treatment response to nCRT in ESCC, and benefit clinical decision making of therapeutic strategy.