Magnetic resonance imaging based on radiomics for differentiating T1-category nasopharyngeal carcinoma from nasopharyngeal lymphoid hyperplasia: a multicenter study.

PURPOSE: To investigate the role of magnetic resonance imaging (MRI) based on radiomics using T2-weighted imaging fat suppression (T2WI-FS) and contrast enhanced T1-weighted imaging (CE-T1WI) sequences in differentiating T1-category nasopharyngeal carcinoma (NPC) from nasopharyngeal lymphoid hyperplasia (NPH). MATERIALS AND METHODS: This study enrolled 614 patients (training dataset: n = 390, internal validation dataset: n = 98, and external validation dataset: n = 126) of T1-category NPC and NPH. Three feature selection methods were used, including analysis of variance, recursive feature elimination, and relief. The logistic regression classifier was performed to construct the radiomics signatures of T2WI-FS, CE-T1WI, and T2WI-FS + CE-T1WI to differentiate T1-category NPC from NPH. The performance of the optimal radiomics signature (T2WI-FS + CE-T1WI) was compared with those of three radiologists in the internal and external validation datasets. RESULTS: Twelve, 15, and 15 radiomics features were selected from T2WI-FS, CE-T1WI, and T2WI-FS + CE-T1WI to develop the three radiomics signatures, respectively. The area under the curve (AUC) values for radiomics signatures of T2WI-FS + CE-T1WI and CE-T1WI were significantly higher than that of T2WI-FS (AUCs = 0.940, 0.935, and 0.905, respectively) for distinguishing T1-category NPC and NPH in the training dataset (Ps all < 0.05). In the internal and external validation datasets, the radiomics signatures based on T2WI-FS + CE-T1WI and CE-T1WI outperformed T2WI-FS with no significant difference (AUCs = 0.938, 0.925, and 0.874 for internal validation dataset and 0.932, 0.918, and 0.882 for external validation dataset; Ps > 0.05). The radiomics signature of T2WI-FS + CE-T1WI significantly performed better than three radiologists in the internal and external validation datasets. CONCLUSION: The MRI-based radiomics signature is meaningful in differentiating T1-category NPC from NPH and potentially helps clinicians select suitable therapy strategies.

Induction sintilimab and chemotherapy followed by concurrent chemoradiotherapy for locally advanced esophageal cancer: a proof-of-concept, single-arm, multicenter, phase 2 trial.

Background: Concurrent chemoradiotherapy is the standard nonoperative treatment for locally advanced esophageal squamous cell carcinoma. However, local recurrence is still the main failure pattern, accounting for more than half of all treatment failures, indicating that the sensitivity of radiotherapy still needs to be improved. This trial aimed at demonstrating whether PD-1 inhibitors followed by chemoradiotherapy could promote esophageal tumor vascular normalization, alleviate hypoxia, and thus enhance radiosensitivity and improve local control. Methods: We did a multicenter, single-arm, phase 2 trial in China. Patients with locally advanced esophageal cancer were enrolled in this study. In induction phase, patients received two cycles of sintilimab, paclitaxel and carboplatin once per 21 days. In concurrent phase, patients were treated with five cycles of carboplatin and paclitaxel once per week concurrent with radiotherapy of 50.4Gy delivered in 28 fractions. The primary endpoint was 2-year local control rate. Hypoxia and vessel normalization was assessed before and after induction phase using immunofluorescence and perfusion CT. This trial is registered with ClinicalTrials.gov (NCT03985046). Findings: Seventy-five patients with esophageal cancer were enrolled in this study between October 2019 and April 2021. The median follow-up of surviving patients was 33.6 months (IQR 29.3-35.7). The 2-year local control rate was 81.7% (95% confidence interval, 72.7%-90.7%), which was much higher than that in concurrent chemoradiation only (71.3%) in previous studies. Vascular normalization and hypoxia alleviation were observed in both biopsy specimens and perfusion CT. Interpretation: The addition of induction immunotherapy to standard concurrent chemoradiotherapy could improve radiosensitivity for locally advanced esophageal cancer as non-surgical treatment. New treatment combination led to higher local control rate through promoting vascular normalization and alleviating hypoxia. Our findings suggest that induction immunotherapy followed by concurrent chemoradiotherapy could be a potential option in future treatment. Funding: National Natural Science Foundation of China and Shanghai Rising-Star Program.

Cascade Catalytic Nanoparticles Selectively Alkalize Cancerous Lysosomes to Suppress Cancer Progression and Metastasis.

Lysosomes are critical in modulating the progression and metastasis for various cancers. There is currently an unmet need for lysosomal alkalizers that can selectively and safely alter the pH and inhibit the function of cancer lysosomes. Here an effective, selective, and safe lysosomal alkalizer is reported that can inhibit autophagy and suppress tumors in mice. The lysosomal alkalizer consists of an iron oxide core that generates hydroxyl radicals (•OH) in the presence of excessive H+ and hydrogen peroxide inside cancer lysosomes and cerium oxide satellites that capture and convert •OH into hydroxide ions. Alkalized lysosomes, which display impaired enzyme activity and autophagy, lead to cancer cell apoptosis. It is shown that the alkalizer effectively inhibits both local and systemic tumor growth and metastasis in mice. This work demonstrates that the intrinsic properties of nanoparticles can be harnessed to build effective lysosomal alkalizers that are both selective and safe.

Ultrasound-enhanced cascade chemodynamic tumor nanotherapy with lactic acid-enabled hydrogen peroxide self-production.

Chemodynamic therapy (CDT) is an effective therapeutic modality for cancer treatment with the action of a catalytic Fenton-like chemoreactive process. To furnish sufficient hydrogen peroxide (H2O2) for CDT, catalysts similar to superoxide dismutase are designed to be in cooperation with nanoplatforms. In this work, we rationally integrate lactate oxidase (LOD) with ultrasmall superparamagnetic iron oxide nanoparticles (USPION) to achieve high efficiency of the cascade Fenton reaction for efficient tumor therapy. During the sequential reaction, LOD converts lactic acid into H2O2 and pyruvate (PA) in situ, and then USPION with peroxidase-like activity generates large amounts of toxic hydroxyl radicals (˙OH) under the action of H2O2. Moreover, the reaction effectively utilizes the excess lactic acid of the tumor microenvironment (TME) as a new target of cancer treatment. To further achieve high-performance tumor treatment, ultrasound has been introduced for augmenting this specific chemoreactive tumor therapy, which can affect cancer cells mainly through sonoporation, cavitation, and thermal effect. With the effects of ultrasound irradiation, this work has constructed an efficient oncology treatment system for tumors. Moreover, the presence of USPION is highly desirable for contrast-enhanced T1-weighted MRI for monitoring the therapeutic process of cancer in real time.

Development and validation of a clinicoradiomic nomogram to assess the HER2 status of patients with invasive ductal carcinoma.

BACKGROUND: The determination of HER2 expression status contributes significantly to HER2-targeted therapy in breast carcinoma. However, an economical, efficient, and non-invasive assessment of HER2 is lacking. We aimed to develop a clinicoradiomic nomogram based on radiomics scores extracted from multiparametric MRI (mpMRI, including ADC-map, T2W1, DCE-T1WI) and clinical risk factors to assess HER2 status. METHODS: We retrospectively collected 214 patients with pathologically confirmed invasive ductal carcinoma between January 2018 to March 2021 from Fudan University Shanghai Cancer Center, and randomly divided this cohort into training set (n = 128, 42 HER2-positive and 86 HER2-negative cases) and validation set (n = 86, 28 HER2-positive and 58 HER2-negative cases) at a ratio of 6:4. The original and transformed pretherapy mpMRI images were treated by semi-automated segmentation and manual modification on the DeepWise scientific research platform v1.6 ( http://keyan.deepwise.com/ ), then radiomics feature extraction was implemented with PyRadiomics library. Recursive feature elimination (RFE) based on logistic regression (LR) and LASSO regression were adpoted to identify optimal features before modeling. LR, Linear Discriminant Analysis (LDA), support vector machine (SVM), random forest (RF), naive Bayesian (NB) and XGBoost (XGB) algorithms were used to construct the radiomics signatures. Independent clinical predictors were identified through univariate logistic analysis (age, tumor location, ki-67 index, histological grade, and lymph node metastasis). Then, the radiomics signature with the best diagnostic performance (Rad score) was further combined with significant clinical risk factors to develop a clinicoradiomic model (nomogram) using multivariate logistic regression. The discriminative power of the constructed models were evaluated by AUC, DeLong test, calibration curve, and decision curve analysis (DCA). RESULTS: 70 (32.71%) of the enrolled 214 cases were HER2-positive, while 144 (67.29%) were HER2-negative. Eleven best radiomics features were retained to develop 6 radiomcis classifiers in which RF classifier showed the highest AUC of 0.887 (95%CI: 0.827-0.947) in the training set and acheived the AUC of 0.840 (95%CI: 0.758-0.922) in the validation set. A nomogram that incorporated the Rad score with two selected clinical factors (Ki-67 index and histological grade) was constructed and yielded better discrimination compared with Rad score (p = 0.374, Delong test), with an AUC of 0.945 (95%CI: 0.904-0.987) in the training set and 0.868 (95%CI: 0.789-0.948; p = 0.123) in the validation set. Moreover, calibration with the p-value of 0.732 using Hosmer-Lemeshow test demonstrated good agreement, and the DCA verified the benefits of the nomogram. CONCLUSION: Post largescale validation, the clinicoradiomic nomogram may have the potential to be used as a non-invasive tool for determination of HER2 expression status in clinical HER2-targeted therapy prediction.

Prediction Breast Molecular Typing of Invasive Ductal Carcinoma Based on Dynamic Contrast Enhancement Magnetic Resonance Imaging Radiomics Characteristics: A Feasibility Study.

Objective: To investigate the feasibility of radiomics in predicting molecular subtype of breast invasive ductal carcinoma (IDC) based on dynamic contrast enhancement magnetic resonance imaging (DCE-MRI). Methods: A total of 303 cases with pathologically confirmed IDC from January 2018 to March 2021 were enrolled in this study, including 223 cases from Fudan University Shanghai Cancer Center (training/test set) and 80 cases from Shaoxing Central Hospital (validation set). All the cases were classified as HR+/Luminal, HER2-enriched, and TNBC according to immunohistochemistry. DCE-MRI original images were treated by semi-automated segmentation to initially extract original and wavelet-transformed radiomic features. The extended logistic regression with least absolute shrinkage and selection operator (LASSO) penalty was applied to identify the optimal radiomic features, which were then used to establish predictive models combined with significant clinical risk factors. Receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis were adopted to evaluate the effectiveness and clinical benefit of the models established. Results: Of the 223 cases from Fudan University Shanghai Cancer Center, HR+/Luminal cancers were diagnosed in 116 cases (52.02%), HER2-enriched in 71 cases (31.84%), and TNBC in 36 cases (16.14%). Based on the training set, 788 radiomic features were extracted in total and 8 optimal features were further identified, including 2 first-order features, 1 gray-level run length matrix (GLRLM), 4 gray-level co-occurrence matrices (GLCM), and 1 3D shape feature. Three multi-class classification models were constructed by extended logistic regression: clinical model (age, menopause, tumor location, Ki-67, histological grade, and lymph node metastasis), radiomic model, and combined model. The macro-average areas under the ROC curve (macro-AUC) for the three models were 0.71, 0.81, and 0.84 in the training set, 0.73, 0.81, and 0.84 in the test set, and 0.76, 0.82, and 0.83 in the validation set, respectively. Conclusion: The DCE-MRI-based radiomic features are significant biomarkers for distinguishing molecular subtypes of breast cancer noninvasively. Notably, the classification performance could be improved with the fusion analysis of multi-modal features.

Redox chemistry-enabled stepwise surface dual nanoparticle engineering of 2D MXenes for tumor-sensitive T1 and T2 MRI-guided photonic breast-cancer hyperthermia in the NIR-II biowindow.

With the fast advent of two-dimensional (2D) MXenes, several therapeutic paradigms based on 2D MXenes flourish, but a generic strategy for MXene functionalization to achieve theranostic functionalities and desirable performance is still lacking. In this work, we report a facile and efficient stepwise surface-functionalization strategy to achieve distinct tumor microenvironment (TME)-responsive T1 and T2 magnetic resonance (MR) imaging-guided photothermal breast-cancer hyperthermia in the second near-infrared (NIR-II) biowindow. This approach is based on the stepwise growth of superparamagnetic Fe3O4 and paramagnetic MnOx nanocomponents onto the large surface of ultrathin 2D niobium carbide (Nb2C) MXene nanosheets (Fe3O4/MnOx-Nb2C) by making full use of the redox status/chemistry of the 2D MXene surface. Such a surface-nanoparticle engineering strategy endows Fe3O4/MnOx-Nb2C composite nanosheets with a series of properties that include high photothermal-conversion efficiency in the NIR-II biowindow (1064 nm, η 30.9%) for effective photothermal tumor eradication without further reoccurrence. It also allows TME-responsive T1- and T2-weighted MR imaging and high biocompatibility for guaranteeing further potential clinical transformation. This work not only makes the efficient diagnostic T1- and T2-weighted MR imaging-guided photonic hyperthermia of breast cancer possible, but also broadens the biomedical applications of MXene-based nanoplatforms by developing novel surface-engineering strategies to construct 2D Nb2C MXene-based composite multifunctional nanoplatforms.

The longitudinal associations between bone mineral density and appendicular skeletal muscle mass in Chinese community-dwelling middle aged and elderly men.

BACKGROUND: The present study aimed to investigate longitudinal associations between bone mineral densities (BMDs) and appendicular skeletal muscle (ASM) mass in different regions of the body using three different indicators, in Chinese community-dwelling middle-aged and elderly men. METHODS: A total of 1,343 men aged ≥ 40 years from a Chinese community were assessed at baseline (2014-2016), one-year follow-up (2016-2017; n = 648), two-year follow-up (2017-2018; n = 407), and three-year follow up (2018-2019; n = 208). At all the four time-points, measurements included ASM mass and BMDs for all regions of the body using dual-energy X-ray absorptiometry. A questionnaire was completed by patients and biochemical markers were assessed. We applied three different indicators to define ASM mass or lean mass respectively, including the appendicular skeletal muscle index (ASM adjusted by height, ASMI, according to the Asian Working Group for Sarcopenia), skeletal muscle index (ASM adjusted by weight, SMI, according to the International Working Group on Sarcopenia), and the appendicular skeletal muscle/body mass index (ratio of ASM and Body mass index (BMI), ASM/BMI, according to the Foundation for the National Institutes of Health). After adjusting for potential confounders, the generalized additive mixed model (GAMM) was used to analyze the trend in ASM mass over time, and to test the association between ASM mass and regional and whole-body BMDs. RESULTS: The incidence of low lean mass was 8.2% defined by ASMI, 16.3% defined by SMI, and 8.3% defined by ASM/BMI. There was a linear relationship between BMDs and ASM mass, and ASMI, ASM/BMI, and SMI gradually decreased with time. After adjusting for covariances, GAMM analysis determined longitudinal associations between BMDs and ASM mass by three indicators respectively: the skull BMD was negatively associated with ASM mass. For each unit increase in skull BMD, ASMI decreased by 0.28 kg/m2 (95% confidence interval (CI) [-0.39 to -0.16]), ASM/BMI decreased by 0.02 m2 (95% CI [-0.03 to -0.00]), and SMI decreased by 0.01% (95% CI[-0.01 to -0.00]). The remaining parameters (including whole-body mean BMD, thoracic spinal BMD, lumbar spinal BMD, hip BMD, femoral neck BMD, pelvic BMD, left arm BMD, right arm BMD, left leg BMD, right leg BMD) were positively correlated with ASM mass. The ASMI increased by 3.07 kg/m2for each unit increase in the femoral neck BMD (95% CI [2.31-3.84]). The ASM/BMI increased by 0.22 m2for each unit increase in the left arm BMD (95% CI [0.12-0.33]), and the SMI increased by 0.05% per unit increase in the left arm BMD (95% CI [0.02-0.08]). CONCLUSIONS: Compared to ASMI and ASM/BMI, SMI was more sensitive to screen for the low lean mass. Skull BMD was negatively associated with ASM mass, while BMDs throughout the rest of the body were positively correlated with ASM mass among the middle-aged and elderly Chinese men.

The value of bi-exponential and non-Gaussian distribution diffusion-weighted imaging in the differentiation of recurrent soft tissue neoplasms and post-surgical changes.

BACKGROUND: Many researches focused on the quantitative mono-exponential diffusion-weighted imaging (DWI) in the assessment of soft tissue neoplasms (STN), but few focused on the value of bi-exponential and non-Gaussian DWI in the application of Recurrent Soft Tissue Neoplasms (RSTN). This study aimed to explore the feasibility of bi-exponential decay and non-Gaussian distribution DWI in the differentiation of RSTN and Post-Surgery Changes (PSC), and compared with mono-exponential DWI. METHODS: The clinical, mono-exponential, bi-exponential [intravoxel incoherent motion (IVIM)] and non-Gaussian [diffusion kurtosis imaging (DKI)] DWI imaging of a cohort of 27 patients [15 RSTN (22 masses), and 12 PSC (12 lesions)] with 34 masses, from Nov 01 2017 to Sep 30 2018, were reviewed. The differences of apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f), mean diffusivity (MD), and mean kurtosis (MK) values were compared between RSTN and PSC groups. The mono-, bi-exponential, and non-Gaussian distribution based predictive models for RSTN and PSC were built and compared. ROC curves were generated and compared by the DeLong test. RESULTS: Intra-class correlation coefficient (ICC) of all IVIM/DKI parameters was high (≥0.841). There were significant differences in ADC, D, f, MD, and MK values between RSTN and PSC, but no difference in D* value. The ADC_IVIM, D, f and MD values of RSTN were lower than those of PSC, but with higher MK value. The ADC_IVIM and D values did better than f value in differentiating these two groups (P<0.05). While there was no significant difference in AUCs among ADC_DKI, MD, and MK values. Also, no significant difference was detected in AUCs between bi-exponential and mono-exponential (P=0.38), or between mono-exponential and non-Gaussian distribution based prediction models (P=0.09). CONCLUSIONS: ADC, D, f, MD, and MK values can be used in the differentiation of RSTN and PSC.

Development and validation of a CT-texture analysis nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes.

BACKGROUND: Thymic epithelial tumors (TETs) are the most common primary tumors in the anterior mediastinum, which have considerable histologic heterogeneity. This study aimed to develop and validate a nomogram based on computed tomography (CT) and texture analysis (TA) for preoperatively predicting the pathological classifications for TET patients. METHODS: Totally TET 172 patients confirmed by postoperative pathology between January 2011 to April 2019 were retrospectively analyzed and randomly divided into training (n = 120) and validation (n = 52) cohorts. Preoperative clinical factors, CT signs and texture features of each patient were analyzed, and prediction models were developed using the least absolute shrinkage and selection operator (LASSO) regression. The performance of the models was evaluated and compared by the area under receiver-operator characteristic (ROC) curve (AUC) and the DeLong test. The clinical application value of the models was determined via the decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and validated using the calibration plots. RESULTS: Totally 87 patients with low-risk TET (LTET) (types A, AB, B1) and 85 patients with high-risk TET (HTET) (types B2, B3, C) were enrolled in this study. We separately constructed 4 prediction models for differentiating LTET from HTET using clinical, CT, texture features, and their combination. These 4 prediction models achieved AUCs of 0.66, 0.79, 0.82, 0.88 in the training cohort and 0.64, 0.82, 0.86, 0.94 in the validation cohort, respectively. The DeLong test and DCA showed that the Combined model, consisting of 2 CT signs and 2 texture parameters, held the highest predictive efficiency and clinical utility (p < 0.05). A prediction nomogram was subsequently developed using the 4 independently risk factors from the Combined model. The calibration curves indicated a good consistency between the actual observations and nomogram predictions for differentiating TET classifications. CONCLUSION: A prediction nomogram incorporating both the CT and texture parameters was constructed and validated in our study, which can be conveniently used for the preoperative individualized prediction of the simplified histologic subtypes in TET patients.

Fabrication and Photocatalytic Activity of Ag3PO4/T-ZnOw Heterostructures.

The Ag3PO4/tetrapod-like ZnO whisker (T-ZnOw) heterostructures were prepared via a simple precipitation method. The obtained heterostructures were characterized by X-ray diffraction, scanning electron microscopy, transmission electron microscopy, high-resolution transmission electron microscopy, X-ray photoelectron spectroscopy, and UV-Vis diffuse reflectance spectroscopy. The photodegradation activity of Ag3PO4/T-ZnOw was evaluated by the degradation of Rhodamine B (RhB) under visible light irradiation. When the molar ratio of Ag3PO4 to T-ZnOw was 10% (Ag3PO4/T-ZnOw-2), the highest degradation efficiency (92.9%) could be achieved among the heterostructures. The photodegradation rate constant of Ag3PO4/T-ZnOw-2 (0.05179 min-1) was 3.59 times that of T-ZnOw (0.01444 min-1). Besides, the Ag3PO4/T-ZnOw-2 photocatalyst still possessed a degradation efficiency of 77.8% after four successive cycles. The Ag3PO4/T-ZnOw-2 catalyst had much higher photocatalytic activity than pure T-ZnOw and better stability and reusability than pure Ag3PO4. The effect of different scavengers on degradation efficiency was investigated, and the possible photocatalytic mechanism of the Ag3PO4/T-ZnOw photocatalyst was also put forward.

Defect engineering of 2D BiOCl nanosheets for photonic tumor ablation.

Photothermal therapy (PTT) is an emerging technology as a noninvasive therapeutic modality for inducing photonic cancer hyperthermia. However, current photothermal conversion agents suffer from low therapeutic efficiency and single functionality. Engineering crystal defects on the surface or substrate of semiconductors can substantially enhance their optical absorption capability as well as improve their photothermal effects in theranostic nanomedicines. In this study, a specific defect engineering strategy was developed to endow two-dimensional (2D) BiOCl nanosheets with intriguing photothermal conversion performance by creating oxygen vacancies on the surface (O-BiOCl). Importantly, the photothermal performance and photoacoustic imaging capability of the 2D O-BiOCl nanosheets could be precisely controlled by modulating the amounts of oxygen vacancies. The strong Bi-based X-ray attenuation coefficient endowed these nanosheets with the contrast-enhanced computed tomography imaging capability. The high near-infrared-triggered photonic hyperthermia for tumor ablation was systematically demonstrated both in vitro at the cellular level and in vivo for tumor breast cancer mice xenograft models. Based on the demonstrated high biocompatibility of these 2D O-BiOCl nanosheets, this work not only formulates an intriguing 2D photothermal nanoagent for tumor ablation, but also provides an efficient strategy to control the photothermal performance of nanoagents by defect engineering.

Development and validation of a nomogram based on CT images and 3D texture analysis for preoperative prediction of the malignant potential in gastrointestinal stromal tumors.

BACKGROUND: Gastrointestinal stromal tumors (GISTs), which are the most common mesenchymal tumors of the digestive system, are treated varyingly according to the malignancy. The purpose of this study is to develop and validate a nomogram for preoperative prediction of the malignant potential in patients with GIST. METHODS: A total of 440 patients with pathologically confirmed GIST after surgery in our hospital from January 2011 to July 2019 were retrospectively analyzed. They were randomly divided into the training set (n = 308) and validation set (n = 132). CT signs and texture features of each patient were analyzed and predictive model were developed using the least absolute shrinkage and selection operator (lasso) regression. Then a nomogram based on selected parameters was developed. The predictive effectiveness of nomogram was evaluated by the area under receiver operating characteristic (ROC) curve (AUC). Concordance index (C-index) and calibration plots were formulated to evaluate the reliability and accuracy of the nomogram by bootstrapping based on internal (training set) and external (validation set) validity. The clinical application value of the nomogram was determined through the decision curve analysis (DCA). RESULTS: Totally 156 GIST patients with low-malignant (very low and low risk) and 284 ones with high-malignant potential (intermediate and high risk) are enrolled in this study. The prediction nomogram consisting of size, cystoid variation and meanValue had an excellent discrimination both in training and validation sets (AUCs (95% confidence interval(CI)): 0.935 (0.908, 0.961), 0.933 (0.892, 0.974); C-indices (95% CI): 0.941 (0.912, 0.956), 0.935 (0.901, 0.982); sensitivity: 81.4, 90.6%; specificity: 75.0, 75.7%; accuracy: 88.0, 88.6%, respectively). The calibration curves indicated a good consistency between the actual observation and nomogram prediction for differentiating GIST malignancy. Decision curve analysis demonstrated that the nomogram was clinically useful. CONCLUSION: This study presents a prediction nomogram that incorporates the CT signs and texture parameter, which can be conveniently used to facilitate the preoperative individualized prediction of malignancy in GIST patients.

Value of Dual-Energy Computed Tomography for Diagnosing Cervical Lymph Node Metastasis in Patients With Papillary Thyroid Cancer.

OBJECTIVE: The objective of this study was to determine the value of dual-energy computed tomography (DECT) for the diagnosis of cervical lymph node metastasis in papillary thyroid cancer. METHODS: The normalized iodine concentration (NIC) and the slope of the spectral Hounsfield unit curve (λHU) in the arterial and venous phases were measured using iodine-overlay images and spectral curves. Quantitative DECT data and qualitative conventional CT data were analyzed by radiologists. RESULTS: The best qualitative parameter for lymph node metastasis detection was obvious node enhancement, and the best quantitative parameter for detection was arterial-phase NIC, which showed high sensitivity, specificity, and accuracy values at an optimal threshold of 25.8%. The best combination of qualitative and quantitative parameters consisted of obvious enhancement and arterial-phase NIC; this combination showed a sensitivity of 90.8% and a specificity of 80.5%. CONCLUSIONS: The DECT quantitative parameters NIC and λHU can be an additional tool to diagnose cervical lymph node metastasis.

[Digital breast tomosynthesis in diagnosis of dense breast lesions].

OBJECTIVE: To evaluate the value of digital breast tomosynthesis (DBT) in diagnosis of dense breast lesions. METHODS: Clinical and pathological data of 163 patients (58 benign lesions, 122 malignant lesions, and 180 lesions in total) with breast lesions undergoing surgical treatment in Shaoxing Central Hospital from January 2017 to December 2018 were retrospectively analyzed. The lesions were classified into non-homogeneous dense gland type and extremely dense gland type according to BI-RADS creterion. Breast MRI and DBT examinations were performed before the surgery. ROC curve was generated and the diagnostic efficacy of two examination methods for dense breast lesions was evaluated with pathological results as the gold standard. The detection rate, diagnostic accuracy of benign and malignant breast lesions were compared between two methods using chi-square test. The accuracy of lesion size preoperatively evaluated by MRI and DBT was analyzed by Pearson correlation. RESULTS: The detection rate and diagnostic accuracy for benign breast lesions by MRI were higher than those by DBT (91.4% vs. 75.9%, χ2=5.098, P<0.05 and 89.7% vs. 67.2%, χ2=8.617, P<0.01). But there were no significant differences in detection rate and accuracy for malignant lesions by MRI and DBT (98.4% vs. 95.1%, χ2=2.068, P>0.05 and 94.3% vs. 91.8%, χ2=0.569, P>0.05). The areas under the ROC curves of MRI, DBT based on BI-RADS classification were 0.910 and 0.832, respectively (Z=1.860, P>0.05). The sensitivities of MRI, DBT to breast lesions were 93.3% and 86.7%, and the specificities were 68.3% and 79.1%. DBT and MRI measurements were positively correlated with pathological measurements (r=0.887 and 0.949, all P<0.01). CONCLUSIONS: DBT can effectively diagnose benign and malignant breast lesions under dense gland background, and it has similar diagnostic efficacy with MRI for breast malignant lesions.

Clearable Theranostic Platform with a pH-Independent Chemodynamic Therapy Enhancement Strategy for Synergetic Photothermal Tumor Therapy.

Chemodynamic therapy (CDT) is an emerging field, which utilizes intratumoral iron-mediated Fenton chemistry for cancer therapy. However, the slightly acidic tumor environment is improper for the classical Fenton reaction, which is generally energetic in a narrow pH range (e.g., pH = 3-4). Herein, a kind of ultrasmall bovine serum albumin (BSA)-modified chalcopyrite nanoparticles (BSA-CuFeS2 NPs) was synthesized via a facile aqueous biomineralization strategy, which shows high dispersity and biocompatibility. Interestingly, the obtained BSA-CuFeS2 shows a pH-independent Fenton-like reaction, which could exert Fenton-like activity to efficiently generate •OH under a weak acidic tumor environment. Combined with the extraordinarily high photothermal conversion (38.8%), BSA-CuFeS2 shows the synergistic function of high photothermal therapy (PTT) and enhanced CDT, that is, PTT/CDT. Importantly, such ultrasmall BSA-CuFeS2 NPs measuring around 4.9 nm can be quickly cleared out of the body through kidneys and liver, thus effectively avoiding long-term toxicity and systemic toxicity. Moreover, BSA-CuFeS2 NPs can act as an efficient T2-weighted magnetic resonance imaging (MRI) contrast agent to guide tumor ablation in vivo. This work offers a universal approach to boost production •OH by a pH-independent Fenton-like reaction strategy and achieves MRI-guided synergistic enhanced photothermal-CDT for highly efficient tumor treatment.

[Stretched-exponential and mono-exponential diffusion-weighted imaging for assessing the aggressiveness of prostate cancer: A histogram analysis].

OBJECTIVE: To assess the value of stretched-exponential and mono-exponential diffusion-weighted imaging (DWI) in predicting the aggressiveness of PCa. METHODS: This retrospective study included 36 cases of PCa with 48 lesions in the peripheral zone diagnosed by DWI with b-values of 0, 500, 1000, and 2000 s/mm2. We reconstructed the apparent diffusion coefficient (ADC), distributed diffusion coefficient (DDC) and α maps on the post-processing workstation, performed a histogram analysis on the largest slice of PCa on T2WI and Spearman's rank-order analysis on the correlation of the histogram variables with Gleason grade grouping (GG). Then, we assessed the values of the histogram variables in differentiating low-grade from high-grade PCa using the receiver operating characteristic (ROC) curve. RESULTS: The percentile and mean ADCs and DDCs were correlated with GG (ρ: 0.392－0.641) but not the α value, skewnesses and kurtosises (ρ: 0.055－0.266). High-grade PCa exhibited significantly lower 10th-, 25th-, 50th- and 75th-percentile and mean ADCs (490 ± 141, 591 ± 137, 695 ± 137, 781 ± 139 and 888 ± 135 mm2/s) and DDCs (420 ± 146, 534 ± 167, 666 ± 182, 787 ± 190 and 912 ± 175 mm2/s) than low-grade PCa (ADCs: 636 ± 74, 727 ± 86, 825 ± 85, 907 ± 85 and 975 ± 117 mm2/s; DDCs: 542 ± 80, 666 ± 93, 806 ± 108, 910 ± 110 and 1023 ± 105 mm2/s), but there were no statistically significant differences between low- and high-grade PCa in the α value (0.67 ± 0.042 vs 0.64 ± 0.036), kurtosises (ADC 0.105 vs 0.078; DDC －0.027 vs －0.401) or skewnesses (ADC －0.042 vs 0.067; DDC －0.058 vs 0.162). Both 10th-percentile ADCs and DDCs showed a higher efficiency than the mean ones in differentiating high- from low-grade PCa, though with no statistically significant difference (P > 0.05). CONCLUSIONS: Histogram variables DDCs and ADCs, rather than the α value, can be used to predict the aggressiveness of PCa, even more efficiently at the 10th percentile than on the mean.

3D convolutional neural network for differentiating pre-invasive lesions from invasive adenocarcinomas appearing as ground-glass nodules with diameters ≤3 cm using HRCT.

BACKGROUND: Identification of pre-invasive lesions (PILs) and invasive adenocarcinomas (IACs) can facilitate treatment selection. This study aimed to develop an automatic classification framework based on a 3D convolutional neural network (CNN) to distinguish different types of lung cancer using computed tomography (CT) data. METHODS: The CT data of 1,545 patients suffering from pre-invasive or invasive lung cancer were collected from Fudan University Shanghai Cancer Center. All of the data were preprocessed through lung mask extraction and 3D reconstruction to adapt to different imaging scanners or protocols. The general flow for the classification framework consisted of nodule detection and cancer classification. The performance of our classification algorithm was evaluated using a receiver operating characteristic (ROC) analysis, with diagnostic results from three experienced radiologists. RESULTS: The sensitivity, specificity, accuracy, and AUC (area under the ROC curve) values of our proposed automatic classification method were 88.5%, 80.1%, 84.0%, and 89.2%, respectively. The results of the CNN classification method were compared to those of three experienced radiologists. The AUC value of our method (AUC =0.892) was higher than those of all radiologists (radiologist 1: 80.5%; radiologist 2: 83.9%; and radiologist 3: 86.7%). CONCLUSIONS: The 3D CNN-based classification algorithm is a promising tool for the diagnosis of pre-invasive and invasive lung cancer and for the treatment choice decision.

Fe-Au Nanoparticle-Coupling for Ultrasensitive Detections of Circulating Tumor DNA.

Effectiveness of cancer therapy relies heavily on the efficient early diagnosis. Circulating tumor DNA (ctDNA) detection is one of the most clinically meaningful liquid biopsy approaches for the noninvasive cancer early diagnosis, which, unfortunately, cannot be applied as a routine diagnostic tool till a number of obstacles, for example, unsatisfactory specificity and sensitivity, and extremely high costs, are overcome. Here, the first paradigm of nanomaterial's application in the extremely specific, ultrasensitive, and yet economical ctDNA detections is reported based on a facile nanoparticle-coupling strategy without amplification, with which polymerase chain reaction (PCR)-introduced bias and other shortcomings are successfully circumvented. Aiming at seven Kirsten rat sarcoma-2 virus (KRAS) point mutations, the present strategy exhibits high specificity and an ultrahigh sensitivity of detecting as low as 0.1 pg mL-1 of KRAS point mutation without prior PCR amplification. Discriminating KRAS gene mutations in lung adenocarcinoma patients at an extremely low detection limit equivalent to 0.12% mutation relative to wild-type gene is successful. It is envisioned that this nanoparticle-coupling approach could be routinely applied clinically for ultra-early diagnosis and monitoring of diverse malignant tumors, thus facilitating the fight against cancer.