Early Contrast-Enhanced MR for Diagnosing Complete Tumor Response of Locally Advanced Esophageal Squamous Cell Carcinoma After Neoadjuvant Therapy: A Retrospective Comparative Study.

BACKGROUND: This study assessed the performance of early contrast-enhanced magnetic resonance (ECE-MR) in the detecting of complete tumor response (ypT0) in patients with esophageal squamous cell carcinoma following neoadjuvant therapy. PATIENTS AND METHODS: Preoperative MR images of consecutive patients who underwent neoadjuvant therapy and surgical resection were reviewed retrospectively. The accuracy of ECE-MR and T2WI+DWI was evaluated by comparing the findings with pathological results. Receiver operating characteristic curve analysis was used to assess the diagnostic performance, and DeLong method was applied to compare the areas under the curves (AUC). Chi-squared analysis was conducted to explore the difference in pathological changes. RESULTS: A total of 198 patients (mean age 62.6 ± 7.8 years, 166 men) with 201 lesions were included. The AUC of ECE-MR was 0.85 (95% CI 0.79-0.90) for diagnosing ypT1-4, which was significantly higher than that of T2WI+DWI (AUC 0.69, 95% CI 0.63-0.76, p < 0.001). The diagnostic performance of both T2WI+DWI and ECE-MR improved with increasing tumor stage. The AUCs of ECE-MRI were higher in ypT1 and ypT2 tumors than T2WI+DWI. Degree 2-3 tumor-infiltrating lymphocytes and neutrophils were commonly seen in ypT0 tumors misdiagnosed by ECE-MR. CONCLUSIONS: Visual evaluation of ECE-MR is a promising diagnostic protocol for the detection of complete tumor response, especially for differentiation with early stage tumors. The accurate diagnosis of complete tumor response after neoadjuvant therapy using imaging modalities is of important significance for clinical decision-making for patients with esophageal squamous cell carcinoma. It is hoped that early contrast-enhanced MR will provide supportive advice for the development of individualized treatment options for patients.

Fully semantic segmentation for rectal cancer based on post-nCRT MRl modality and deep learning framework.

PURPOSE: Rectal tumor segmentation on post neoadjuvant chemoradiotherapy (nCRT) magnetic resonance imaging (MRI) has great significance for tumor measurement, radiomics analysis, treatment planning, and operative strategy. In this study, we developed and evaluated segmentation potential exclusively on post-chemoradiation T2-weighted MRI using convolutional neural networks, with the aim of reducing the detection workload for radiologists and clinicians. METHODS: A total of 372 consecutive patients with LARC were retrospectively enrolled from October 2015 to December 2017. The standard-of-care neoadjuvant process included 22-fraction intensity-modulated radiation therapy and oral capecitabine. Further, 243 patients (3061 slices) were grouped into training and validation datasets with a random 80:20 split, and 41 patients (408 slices) were used as the test dataset. A symmetric eight-layer deep network was developed using the nnU-Net Framework, which outputs the segmentation result with the same size. The trained deep learning (DL) network was examined using fivefold cross-validation and tumor lesions with different TRGs. RESULTS: At the stage of testing, the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD) were applied to quantitatively evaluate the performance of generalization. Considering the test dataset (41 patients, 408 slices), the average DSC, HD95, and MSD were 0.700 (95% CI: 0.680-0.720), 17.73 mm (95% CI: 16.08-19.39), and 3.11 mm (95% CI: 2.67-3.56), respectively. Eighty-two percent of the MSD values were less than 5 mm, and fifty-five percent were less than 2 mm (median 1.62 mm, minimum 0.07 mm). CONCLUSIONS: The experimental results indicated that the constructed pipeline could achieve relatively high accuracy. Future work will focus on assessing the performances with multicentre external validation.

Distinct antiviral activities of IFNφ1 and IFNφ4 in zebrafish.

Interferons (IFNs) are a group of secreted cytokines that play a crucial role in antiviral immunity. Type I IFNs display functional disparities. In teleosts, type I IFNs are categorized into two subgroups containing one or two pairs of disulfide bonds. However, their functional differences have not been fully elucidated. In this study, we comparatively characterized the antiviral activities of zebrafish IFNφ1 and IFNφ4 belonging to the group I type I IFNs. It was found that ifnφ1 and ifnφ4 were differentially modulated during viral infection. Although both IFNφ1 and IFNφ4 activated JAK-STAT signaling pathway via CRFB1/CRFB5 receptor complex, IFNφ4 was less potent in inducing phosphorylation of STAT1a, STAT1b and STAT2 and the expression of antiviral genes than IFNφ1, thereby conferring weaker antiviral resistance of target cells. Taken together, our results provide insights into the functional divergence of type I IFNs in lower vertebrates.

CT radiomics features of meso-esophageal fat in predicting overall survival of patients with locally advanced esophageal squamous cell carcinoma treated by definitive chemoradiotherapy.

OBJECTIVE: To investigate the value of CT radiomics features of meso-esophageal fat in the overall survival (OS) prediction of patients with locally advanced esophageal squamous cell carcinoma (ESCC). METHODS: A total of 166 patients with locally advanced ESCC in two medical centers were retrospectively analyzed. The volume of interest (VOI) of meso-esophageal fat and tumor were manually delineated on enhanced chest CT using ITK-SNAP. Radiomics features were extracted from the VOIs by Pyradiomics and then selected using the t-test, the Cox regression analysis, and the least absolute shrinkage and selection operator. The radiomics scores of meso-esophageal fat and tumors for OS were constructed by a linear combination of the selected radiomic features. The performance of both models was evaluated and compared by the C-index. Time-dependent receiver operating characteristic (ROC) analysis was employed to analyze the prognostic value of the meso-esophageal fat-based model. A combined model for risk evaluation was constructed based on multivariate analysis. RESULTS: The CT radiomic model of meso-esophageal fat showed valuable performance for survival analysis, with C-indexes of 0.688, 0.708, and 0.660 in the training, internal, and external validation cohorts, respectively. The 1-year, 2-year, and 3-year ROC curves showed AUCs of 0.640-0.793 in the cohorts. The model performed equivalently compared to the tumor-based radiomic model and performed better compared to the CT features-based model. Multivariate analysis showed that meso-rad-score was the only factor associated with OS. CONCLUSIONS: A baseline CT radiomic model based on the meso-esophagus provide valuable prognostic information for ESCC patients treated with dCRT.

Quantitative CT evaluation after two cycles of induction chemotherapy to predict prognosis of patients with locally advanced oesophageal squamous cell carcinoma before undergoing definitive chemoradiotherapy/radiotherapy.

OBJECTIVE: To investigate the performance of quantitative CT analysis in predicting the prognosis of patients with locally advanced oesophageal squamous cell carcinoma (ESCC) after two cycles of induction chemotherapy before definitive chemoradiotherapy/radiotherapy. METHODS: A total of 110 patients with locally advanced ESCC were retrospectively analysed. Baseline chest CT and CT after two cycles of induction chemotherapy were analysed. A multivariate Cox proportional-hazard regression model was used to identify independent prognostic markers for survival analysis. Then, a CT scoring system was established. Time-dependent receiver operating characteristic (ROC) curve analysis and the Kaplan-Meier method were employed for analysing the prognostic value of the CT scoring system. RESULTS: Body mass index, treatment strategy, change ratios of thickness (ΔTHmax), CT value of the primary tumour (ΔCTVaxial) and the short diameter (ΔSD-LN), and the presence of an enlarged small lymph node (ESLN) after two cycles of chemotherapy were noted as independent factors for predicting overall survival (OS). The specificity of the presence of ESLN for death after 12 months was up to 100%. Areas under the curve value of the CT scoring system for predicting OS and progression-free survival (PFS) were higher than that of the RECIST (p < 0.05). Responders had significantly longer OS and PFS than non-responders. CONCLUSION: Quantitative CT analysis after two cycles of induction chemotherapy could predict the outcome of locally advanced ESCC patients treated with definitive chemoradiotherapy/radiotherapy. The CT scoring system could contribute to the development of an appropriate strategy for patients with locally advanced ESCC. KEY POINTS: • Quantitative CT evaluation after two cycles of induction chemotherapy can predict the long-term outcome of locally advanced oesophageal cancer treated with definitive chemoradiotherapy/radiotherapy. • A CT scoring system provides valuable imaging support for indicating the prognosis at the early stage of therapy. • Quantitative CT evaluation can assist clinicians in personalising treatment plans.

LncRNA DNAJC3-AS1 promotes the biological functions of papillary thyroid carcinoma via regulating the microRNA-27a-3p/CCBE1 axis.

Long noncoding RNA DNAJC3-AS1 (lncRNA DNAJC3-AS1) has been probed in many studies, while the regulatory mechanism of DNAJC3-AS1 on papillary thyroid carcinoma (PTC) via regulating microRNA (miR)-27a-3p remains inadequate. This research aims to depict the role of DNAJC3-AS1, miR-27a-3p, collagen, and calcium-binding EGF domain-containing protein 1 (CCBE1) on PTC development. DNAJC3-AS1, miR-27a-3p, and CCBE1 expression levels in PTC tissues and adjacent normal tissues were tested. The relation of DNAJC3-AS1, miR-27a-3p, and CCBE1 was analyzed. DNAJC3-AS1 and miR-27a-3p and CCBE1-related oligonucleotides were transfected into IHH-4 cells to investigate their role in PTC development. Cell tumorigenicity was detected by in vivo assay. DNAJC3-AS1 and CCBE1 expressed highly and miR-27a-3p expressed lowly in PTC. Downregulation of DNAJC3-AS1, upregulating miR-27a-3p or downregulating CCBE1 impaired the malignant behaviors of IHH-4 cells. Depletion of miR-27a-3p reversed the DNAJC3-AS1 suppression-induced phenotypic inhibition of IHH-4 cells. DNAJC3-AS1 bound to miR-27a-3p and CCBE1 as a target of miR-27a-3p. Our study highlights that DNAJC3-AS1 inhibits miR-27a-3p to promote CCBE1 expression, thereby facilitating PTC development. This study affords distinguished therapeutic strategies and novel research directions for PTC treatment.

IL-4/13 expressing CD3γ/δ+ T cells regulate mucosal immunity in response to Flavobacterium columnare infection in grass carp.

Interleukin (IL) 4 and 13 are signature cytokines orchestrating Th2 immune response. Teleost fish have two homologs, termed IL-4/13A and IL-4/13B, and have been functionally characterized. However, what cells express IL-4/13A and IL-4/13B has not been investigated in fish. In this work, the recombinant IL-4/13A and IL-4/13B proteins of grass carp (Ctenopharyngodon idella) were produced in the Escherichia coli (E. coli) cells and purified. Monoclonal antibodies (mAbs) against the recombinant CiIL-4/13A and CiIL-4/13B proteins were prepared and characterized. Western blotting analysis showed that the CiIL-4/13A and CiIL-4/13B mAbs could specifically recognize the recombinant proteins expressed in the E. coli cells and HEK293T cells and did not cross-react with each other. Confocal microscopy revealed that the CiIL-4/13A+ and CiIL-4/13B+ cells were present in the gills, intestine and spleen and could be upregulated in fish infected with Flavobacterium columnare (F. columnare). Interestingly, the cells expressing CiIL-4/13A and CiIL-4/13B were mostly CD3γ/δ+ cells. The CD3γ/δ+/IL-4/13A+ and CD3γ/δ+/IL-4/13B+ cells were significantly upregulated in the gill filaments and the intestinal mucosa after F. columnare infection. Our results imply that the CD3γ/δ+/IL-4/13A+ and CD3γ/δ+/IL-4/13B+ cells are important for homeostasis and the regulation of mucosal immunity.

IL-27 suppresses spring viremia of carp virus replication in zebrafish.

Interleukin (IL) 27 is a member of the IL-12 family and is a heterodimeric cytokine composed of IL-27A and Epstein-Barr virus-induced 3 (EBI3). It plays an important role in regulating inflammation and cancer progression. IL-27A not only functions by dimerizing with EBI3 but also acts alone. Here, we report that IL-27A and EBI3 suppress spring viremia of carp virus (SVCV) replication in zebrafish. Expression analysis reveals that il-27a and ebi3 were significantly upregulated in the ZF4 cells by SVCV and poly(I:C), and in the zebrafish caudal fin (ZFIN) cells overexpressed with SVCV genes. Interestingly, il-27a and ebi3 were not modulated by IFNφ1, indicating that they are not IFN stimulated genes (ISGs). Furthermore, overexpression of IL-27A and EBI3 alone inhibited SVCV replication in the EPC cells, but less potent than co-expression of IL-27A and EBI3. Intriguingly, IL-27A could not induce the expression of irf3, ifn, isg15 and mx1. Taken together, our results demonstrate that IL-27A and EBI3 activate innate antiviral response in an IFN independent manner in zebrafish.

Zebrafish SETD3 mediated ubiquitination of phosphoprotein limits spring viremia of carp virus infection.

Lysine methylation is a post-translational modification of histone and non-histone proteins and affects numerous cellular processes. The actin histidine methyltransferase SET domain containing 3 (SETD3) is a member of the protein lysine methyltransferase (PKMT) family which catalyse the addition of methyl groups to lysine residues. However, the role of SETD3 in virus-mediated innate immune responses has rarely been investigated. In this study, zebrafish SETD3 was shown to be induced by poly(I:C) and spring viremia of carp virus (SVCV) and inhibited virus infection. Further, it was found that SETD3 directly interacted with SVCV phosphoprotein (SVCV P) in the cytoplasm of EPC cells, initiating ubiquitination to degrade the SVCV P protein via proteasomal pathway. Interestingly, mutants lacking the SET and RSB domains were able to promote degradation of SVCV P, indicating that they are not required for SETD3 mediated degradation of SVCV P. Taken together, our study demonstrates that SETD3 is an antiviral factor which limits virus replication by promoting ubiquitination of viral phosphoprotein and subsequent protein degradation.

The Conversion of MRI Data With Multiple b-Values into Signature-Like Pictures to Predict Treatment Response for Rectal Cancer.

BACKGROUND: Diffusion weighted imaging (DWI) at multiple b-values has been used to predict the pathological complete response (pCR) to neoadjuvant chemoradiotherapy for locally advanced rectal cancer. Non-Gaussian models fit the signal decay of diffusion by several physical values from different approaches of approximation. PURPOSE: To develop a deep learning method to analyze DWI data scanned at multiple b-values independent on Gaussian or non-Gaussian models and to apply to a rectal cancer neoadjuvant chemoradiotherapy model. STUDY TYPE: Retrospective. POPULATION: A total of 472 participants (age: 56.6 ± 10.5 years; 298 males and 174 females) with locally advanced adenocarcinoma were enrolled and chronologically divided into a training group (n = 200; 42 pCR/158 non-pCR), a validation group (n = 72; 11 pCR/61 non-pCR) and a test group (n = 200; 44 pCR/156 non-pCR). FIELD STRENGTH/SEQUENCE: A 3.0 T MRI scanner. DWI with a single-shot spin echo-planar imaging pulse sequence at 12 b-values (0, 20, 50, 100, 200, 400, 600, 800, 1000, 1200, 1400, and 1600 sec/mm2 ). ASSESSMENT: DWI signals from manually delineated tumor region were converted into a signature-like picture by concatenating all histograms from different b-values. Pathological results (pCR/non-pCR) were used as the ground truth for deep learning. Gaussian and non-Gaussian methods were used for comparison. STATISTICAL TESTS: Analysis of variance for age; Chi-square for gender and pCR/non-pCR; area under the receiver operating characteristic (ROC) curve (AUC); DeLong test for AUC. P < 0.05 for significant difference. RESULTS: The AUC in the test group is 0.924 (95% CI: 0.866-0.983) for the signature-like pictures converted from 35 bins, and it is 0.931 (95% CI: 0.884-0.979) for the signature-like pictures converted from 70 bins, which is significantly (Z = 3.258, P < 0.05) larger than Dapp , the best predictor in non-Gaussian methods with AUC = 0.773 (95% CI: 0.682-0.865). DATA CONCLUSION: The proposed signature-like pictures provide more accurate pretreatment prediction of the response to neoadjuvant chemoradiotherapy than the fitted methods for locally advanced rectal cancer. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Correlation Between the Distance to Mesorectal Fascia and Prognosis of cT3 Rectal Cancer: Results of a Multicenter Study From China.

BACKGROUND: The cT3 substage criteria based on extramural depth of tumor invasion in rectal cancer have several limitations. OBJECTIVE: This study proposed that the distance between the deepest tumor invasion and mesorectal fascia on pretherapy MRI can distinguish the prognosis of patients with cT3 rectal cancer. DESIGN: This is a cohort study. SETTING: This study included a prospective, single-center, observational cohort and a retrospective, multicenter, independent validation cohort. PATIENT: Patients who had cT3 rectal cancer with negative mesorectal fascia undergoing neoadjuvant chemoradiotherapy followed by radical surgery were included in 4 centers in China from January 2013 to September 2014. INTERVENTION: Baseline MRI with the distance between the deepest tumor invasion and mesorectal fascia, extramural depth of tumor invasion, and mesorectum thickness were measured. MAIN OUTCOME MEASURES: The cutoff of the distance between the deepest tumor invasion and mesorectal fascia was determined by time-dependent receiver operating characteristic curves, supported by a 5-year progression rate from the prospective cohort, and was then validated in a retrospective cohort. RESULTS: There were 124 and 274 patients included in the prospective and independent validation cohorts. The distance between the deepest tumor invasion and mesorectal fascia was the only predictor for cancer-specific death (HR, 0.1; 95% CI, 0.0-0.7) and was also a significant predictor for distant recurrence (HR, 0.4; 95% CI, 0.2-0.9). No statistically significant difference was observed in prognosis between patients classified as T3a/b and T3c/d. LIMITATIONS: The sample size is relatively small, and the study focused on cT3 rectal cancers with a negative mesorectal fascia. CONCLUSIONS: A cutoff of 7 mm of the distance between the deepest tumor invasion and mesorectal fascia on baseline MRI can distinguish cT3 rectal cancer from a different prognosis. We recommend using the distance between the deepest tumor invasion and mesorectal fascia on baseline MRI for local and systemic risk assessment and providing a tailored schedule of neoadjuvant treatment. See Video Abstract at http://links.lww.com/DCR/B682.CORRELACIÓN ENTRE LA DISTANCIA DE LA FASCIA MESORRECTAL Y EL PRONÓSTICO DEL CÁNCER DE RECTO cT3: RESULTADOS DE UN ESTUDIO MULTICÉNTRICO DE CHINAANTECEDENTES:Los criterios de subestadificación cT3 basados en la profundidad extramural de invasión tumoral en el cáncer de recto tienen varias limitaciones.OBJETIVO:Este estudio propuso que la distancia entre la invasión tumoral más profunda y la fascia mesorrectal en la resonancia magnética preterapia puede distinguir el pronóstico de los pacientes con cT3.DISEÑO:Estudio de cohorte.ENTORNO CLINICO:El estudio incluyó una cohorte observacional, prospectiva, unicéntrica, y una cohorte de validación retrospectiva, multicéntrica e independiente.PACIENTE:Se incluyeron pacientes con cáncer de recto cT3 con fascia mesorrectal negativa sometidos a quimio-radioterapia neoadyuvante seguida de cirugía radical en cuatro centros de China desde enero de 2013 hasta septiembre de 2014.INTERVENCIÓN:Imágenes de resonancia magnética de referencia fueron medidas con la distancia entre la invasión tumoral más profunda y la fascia mesorrectal; la profundidad extramural de la invasión tumoral y el grosor del mesorrecto.PRINCIPALES MEDIDAS DE VALORACION:El límite de la distancia entre la invasión tumoral más profunda y la fascia mesorrectal se determinó mediante curvas características operativas del receptor dependientes del tiempo y se apoyó en la tasa de progresión a 5 años de la cohorte prospectiva, y luego se validó en una cohorte retrospectiva.RESULTADOS:Se incluyeron 124 y 274 pacientes en la cohorte de validación prospectiva e independiente, respectivamente. La distancia entre la invasión tumoral más profunda de la fascia mesorrectal fue el único predictor de muerte específica por cáncer (Hazard ratio: 0.1, 95% CI, 0,0-0,7); y también fue un predictor significativo de recurrencia distante Hazard ratio: 0,4, 95% CI, 0,2-0,9). No se observaron diferencias estadísticamente significativas en el pronóstico entre los pacientes clasificados como T3a/b y T3c/d.LIMITACIONES:El tamaño de la muestra es relativamente pequeño y el estudio se centró en los cánceres de recto cT3 con fascia mesorrectal negativa.CONCLUSIONES:Un límite de 7 mm de distancia entre la invasión tumoral más profunda y la fascia mesorrectal en la resonancia magnética de referencia puede distinguir el cáncer de recto cT3 de diferentes pronósticos. Recomendamos la distancia entre la invasión tumoral más profunda y la fascia mesorrectal en la resonancia magnética de referencia para la evaluación del riesgo local y sistémico, proporcionando un programa personalizado de tratamiento neoadyuvante. Consulte Video Resumen en http://links.lww.com/DCR/B682. (Traducción- Dr. Francisco M. Abarca-Rendon).

PLAAT1 promotes p53 degradation via autophagy-lysosome pathway in zebrafish.

PLAAT1 belongs to the PLAAT family and plays regulatory roles in cell growth, tumor suppression and phospholipid metabolism. However, whether PLAAT1 is involved in p53 mediated signaling has not been investigated. Here, we report that PLAAT1 promotes degradation of p53 in zebrafish. We found that the plaat1 gene was constitutively expressed in tissues including liver, kidney, spleen, intestine, eye and brain, with relative higher expression levels detected in the brain and eye. Overexpression of plaat1 led to inhibition of p53 and tnfα mRNA expression. Furthermore, it was shown that PLAAT1 interacted with p53 to facilitate p53 degradation via autophagy-lysosome dependent pathway. Our work indicates that PLAAT1 is involved in the interplay between p53 mediated cellular responses and autophagy.

Predicting Lymph Node Metastasis Using Computed Tomography Radiomics Analysis in Patients With Resectable Esophageal Squamous Cell Carcinoma.

OBJECTIVES: We investigated the value of radiomics data, extracted from pretreatment computed tomography images of the primary tumor (PT) and lymph node (LN) for predicting LN metastasis in esophageal squamous cell carcinoma (ESCC) patients. MATERIALS AND METHODS: A total 338 ESCC patients were retrospectively assessed. Primary tumor, the largest short-axis diameter LN (LSLN), and PT and LSLN interaction term (IT) radiomic features were calculated. Subsequently, the radiomic signature was combined with clinical risk factors in multivariable logistic regression analysis to build various clinical-radiomic models. Model performance was evaluated with respect to the fit, overall performance, differentiation, and calibration. RESULTS: A clinical-radiomic model, which combined clinical and PT-LSLN-IT radiomic signature, showed favorable discrimination and calibration. The area under curve value was 0.865 and 0.841 in training and test set. CONCLUSIONS: A venous computed tomography radiomic model based on the PT, LSLN, and IT radiomic features represents a novel noninvasive tool for prediction LN metastasis in ESCC.

Automatic segmentation of rectal tumor on diffusion-weighted images by deep learning with U-Net.

PURPOSE: Manual delineation of a rectal tumor on a volumetric image is time-consuming and subjective. Deep learning has been used to segment rectal tumors automatically on T2-weighted images, but automatic segmentation on diffusion-weighted imaging is challenged by noise, artifact, and low resolution. In this study, a volumetric U-shaped neural network (U-Net) is proposed to automatically segment rectal tumors on diffusion-weighted images. METHODS: Three hundred patients of locally advanced rectal cancer were enrolled in this study and divided into a training group, a validation group, and a test group. The region of rectal tumor was delineated on the diffusion-weighted images by experienced radiologists as the ground truth. A U-Net was designed with a volumetric input of the diffusion-weighted images and an output of segmentation with the same size. A semi-automatic segmentation method was used for comparison by manually choosing a threshold of gray level and automatically selecting the largest connected region. Dice similarity coefficient (DSC) was calculated to evaluate the methods. RESULTS: On the test group, deep learning method (DSC = 0.675 ± 0.144, median DSC is 0.702, maximum DSC is 0.893, and minimum DSC is 0.297) showed higher segmentation accuracy than the semi-automatic method (DSC = 0.614 ± 0.225, median DSC is 0.685, maximum DSC is 0.869, and minimum DSC is 0.047). Paired t-test shows significant difference (T = 2.160, p = 0.035) in DSC between the deep learning method and the semi-automatic method in the test group. CONCLUSION: Volumetric U-Net can automatically segment rectal tumor region on DWI images of locally advanced rectal cancer.

Deep learning model improves radiologists' performance in detection and classification of breast lesions.

OBJECTIVE: Computer-aided diagnosis using deep learning algorithms has been initially applied in the field of mammography, but there is no large-scale clinical application. METHODS: This study proposed to develop and verify an artificial intelligence model based on mammography. Firstly, mammograms retrospectively collected from six centers were randomized to a training dataset and a validation dataset for establishing the model. Secondly, the model was tested by comparing 12 radiologists' performance with and without it. Finally, prospectively enrolled women with mammograms from six centers were diagnosed by radiologists with the model. The detection and diagnostic capabilities were evaluated using the free-response receiver operating characteristic (FROC) curve and ROC curve. RESULTS: The sensitivity of model for detecting lesions after matching was 0.908 for false positive rate of 0.25 in unilateral images. The area under ROC curve (AUC) to distinguish the benign lesions from malignant lesions was 0.855 [95% confidence interval (95% CI): 0.830, 0.880]. The performance of 12 radiologists with the model was higher than that of radiologists alone (AUC: 0.852 vs. 0.805, P=0.005). The mean reading time of with the model was shorter than that of reading alone (80.18 s vs. 62.28 s, P=0.032). In prospective application, the sensitivity of detection reached 0.887 at false positive rate of 0.25; the AUC of radiologists with the model was 0.983 (95% CI: 0.978, 0.988), with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 94.36%, 98.07%, 87.76%, and 99.09%, respectively. CONCLUSIONS: The artificial intelligence model exhibits high accuracy for detecting and diagnosing breast lesions, improves diagnostic accuracy and saves time.

Predicting Rectal Cancer Response to Neoadjuvant Chemoradiotherapy Using Deep Learning of Diffusion Kurtosis MRI.

Background Preoperative response evaluation with neoadjuvant chemoradiotherapy remains a challenge in the setting of locally advanced rectal cancer. Recently, deep learning (DL) has been widely used in tumor diagnosis and treatment and has produced exciting results. Purpose To develop and validate a DL method to predict response of rectal cancer to neoadjuvant therapy based on diffusion kurtosis and T2-weighted MRI. Materials and Methods In this prospective study, participants with locally advanced rectal adenocarcinoma (≥cT3 or N+) proved at histopathology and baseline MRI who were scheduled to undergo preoperative chemoradiotherapy were enrolled from October 2015 to December 2017 and were chronologically divided into 308 training samples and 104 test samples. DL models were constructed primarily to predict pathologic complete response (pCR) and secondarily to assess tumor regression grade (TRG) (TRG0 and TRG1 vs TRG2 and TRG3) and T downstaging. Other analysis included comparisons of diffusion kurtosis MRI parameters and subjective evaluation by radiologists. Results A total of 383 participants (mean age, 57 years ± 10 [standard deviation]; 229 men) were evaluated (290 in the training cohort, 93 in the test cohort). The area under the receiver operating characteristic curve (AUC) was 0.99 for the pCR model in the test cohort, which was higher than the AUC for raters 1 and 2 (0.66 and 0.72, respectively; P < .001 for both). AUC for the DL model was 0.70 for TRG and 0.79 for T downstaging. AUC for pCR with the DL model was better than AUC for the best-performing diffusion kurtosis MRI parameters alone (diffusion coefficient in normal diffusion after correcting the non-Gaussian effect [Dapp value] before neoadjuvant therapy, AUC = 0.76). Subjective evaluation by radiologists yielded a higher error rate (1 - accuracy) (25 of 93 [26.9%] and 23 of 93 [24.8%] for raters 1 and 2, respectively) in predicting pCR than did evaluation with the DL model (two of 93 [2.2%]); the radiologists achieved a lower error rate (12 of 93 [12.9%] and 13 of 93 [14.0%] for raters 1 and 2, respectively) when assisted by the DL model. Conclusion A deep learning model based on diffusion kurtosis MRI showed good performance for predicting pathologic complete response and aided the radiologist in assessing response of locally advanced rectal cancer after neoadjuvant chemoradiotherapy. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Koh in this issue.

HutZ is required for biofilm formation and contributes to the pathogenicity of Edwardsiella piscicida.

Edwardsiella piscicida is a severe fish pathogen. Haem utilization systems play an important role in bacterial adversity adaptation and pathogenicity. In this study, a speculative haem utilization protein, HutZEp, was characterized in E. piscicida. hutZEp is encoded with two other genes, hutW and hutX, in an operon that is similar to the haem utilization operon hutWXZ identified in V. cholerae. However, protein activity analysis showed that HutZEp is probably not related to hemin utilization. To explore the biological role of HutZEp, a markerless hutZEp in-frame mutant strain, TX01ΔhutZ, was constructed. Deletion of hutZEp did not significantly affect bacterial growth in normal medium, in iron-deficient conditions, or in the presence of haem but significantly retarded bacterial biofilm growth. The expression of known genes related to biofilm growth was not affected by hutZEp deletion, which indicated that HutZEp was probably a novel factor promoting biofilm formation in E. piscicida. Compared to the wild-type TX01, TX01ΔhutZ exhibited markedly compromised tolerance to acid stress and host serum stress. Pathogenicity analysis showed that inactivation of hutZEp significantly impaired the ability of E. piscicida to invade and reproduce in host cells and to infect host tissue. In contrast to TX01, TX01ΔhutZ was defective in blocking host macrophage activation. The expression of hutZEp was directly regulated by the ferric uptake regulator Fur. This study is the first functional characterization of HutZ in a fish pathogen, and these findings suggested that HutZEp is essential for E. piscicida biofilm formation and contributes to host infection.

Universal stress proteins contribute Edwardsiella piscicida adversity resistance and pathogenicity and promote blocking host immune response.

Universal stress proteins (Usps) exist ubiquitously in bacteria and other organisms. Usps play an important role in adaptation of bacteria to a variety of environmental stresses. There is increasing evidence that Usps facilitate pathogens to adapt host environment and are involved in pathogenicity. Edwardsiella piscicida (formerly included in E. tarda) is a severe fish pathogen and infects various important economic fish including tilapia (Oreochromis niloticus). In E. piscicida, a number of systems and factors that are involved in stress resistance and pathogenesis were identified. However, the function of Usps in E. piscicida is totally unknown. In this study, we examined the expressions of 13 usp genes in E. piscicida and found that most of these usp genes were up-regulated expression under high temperature, oxidative stress, acid stress, and host serum stress. Particularly, among these usp genes, usp13, exhibited dramatically high expression level upon several stress conditions. To investigate the biological role of usp13, a markerless usp13 in-frame mutant strain, TX01Δusp13, was constructed. Compared to the wild type TX01, TX01Δusp13 exhibited markedly compromised tolerance to high temperature, hydrogen peroxide, and low pH. Deletion of usp13 significantly retarded bacterial biofilm growth and decreased resistance against serum killing. Pathogenicity analysis showed that the inactivation of usp13 significantly impaired the ability of E. piscicida to invade into host cell and infect host tissue. Introduction of a trans-expressed usp13 gene restored the lost virulence of TX01Δusp13. In support of these results, host immune response induced by TX01 and TX01Δusp13 was examined, and the results showed reactive oxygen species (ROS) levels in TX01Δusp13-infected macrophages were significantly higher than those in TX01-infected cells. The expression level of several cytokines (IL-6, IL-8, IL-10, TNF-α, and CC2) in TX01Δusp13-infected fish was significantly higher than that in TX01-infected fish. These results suggested that the deletion of usp13 attenuated the ability of bacteria to overcome the host immune response to pathogen infection. Taken together, our study indicated Usp13 of E. piscicida was not only important participant in adversity resistance, but also was essential for E. piscicida pathogenicity and contributed to block host immune response to pathogen infection.

Prediction of pathological nodal stage of locally advanced rectal cancer by collective features of multiple lymph nodes in magnetic resonance images before and after neoadjuvant chemoradiotherapy.

OBJECTIVE: To predict pathological nodal stage of locally advanced rectal cancer by a radiomic method that uses collective features of multiple lymph nodes (LNs) in magnetic resonance images before and after neoadjuvant chemoradiotherapy (NCRT). METHODS: A total of 215 patients were included in this study and chronologically divided into the discovery cohort (n=143) and validation cohort (n=72). In total, 2,931 pre-NCRT LNs and 1,520 post-NCRT LNs were delineated from all visible rectal LNs in magnetic resonance images. Geometric, first-order and texture features were extracted from each LN before and after NCRT. Collective features are defined as the maximum, minimum, mean, median value and standard deviation of each feature from all delineated LNs of each participant. LN-model is constructed from collective LN features by logistic regression model with L1 regularization to predict pathological nodal stage (ypN0 or ypN+). Tumor-model is constructed from tumor features for comparison by using DeLong test. RESULTS: The LN-model selects 7 features from 412 LN features, and the tumor-model selects 7 features from 82 tumor features. The area under the receiver operating characteristic curve (AUC) of LN-model in the discovery cohort is 0.818 [95% confidence interval (95% CI): 0.745-0.878], significantly (Z=2.09, P=0.037) larger than 0.685 (95% CI: 0.602-0.760) of the tumor-model. The AUC of LN-model in validation cohort is 0.812 (95% CI: 0.703-0.895), significantly (Z=3.106, P=0.002) larger than 0.517 (95% CI: 0.396-0.636) of the tumor-model. CONCLUSIONS: The usage of collective features from all visible rectal LNs performs better than the usage of tumor features for the prediction of pathological nodal stage of locally advanced rectal cancer.

MRI of Extramural Venous Invasion in Locally Advanced Rectal Cancer: Relationship to Tumor Recurrence and Overall Survival.

Purpose To study the relationship between MRI-defined extramural venous invasion (EMVI) prior to treatment and prognosis in patients with locally advanced rectal cancer treated with neoadjuvant chemotherapy-radiation therapy followed by surgery. Materials and Methods This retrospective study included 517 patients with locally advanced rectal cancer evaluated from August 2008 to December 2014. Baseline and posttherapy MRI and follow-up data were retrieved for all patients. After training by using 328 cases with pathologic evaluation of EMVI after therapy, radiologists evaluated baseline MRI for EMVI status in addition to tumor size and characteristics, nodal status, and invasion of the mesorectal fascia. Reader reproducibility was determined by using κ coefficient. Kaplan-Meier curves and adjusted Cox models were used to determine the relationship of baseline MRI parameters to overall survival, metastasis-free survival, and local relapse-free survival. Results Among 517 patients, 335 (64.8%) were men; the mean age was 55.6 years ± 11.5 (standard deviation). At baseline, radiologists identified 259 of 517 (50%) patients with EMVI by using MRI. In adjusted analysis, EMVI and mesorectal fascial invasion at baseline MRI were predictors of metastasis-free survival (hazard ratio, 0.3 and 0.6; P ˂ .01 and P ˂ .02, respectively) and overall survival (hazard ratio, 0.5 and 0.5; P = .01 and P = .02, respectively). EMVI was the only factor associated with local relapse-free survival (hazard ratio, 0.3; P ˂ .01). The κ coefficient for determination of EMVI was 0.80. Conclusion Extramural venous invasion (EMVI) can be reliably evaluated with MRI. The presence of EMVI was associated with greater risk of local and distant tumor recurrence and overall death in patients with locally advanced rectal cancer treated with neoadjuvant chemotherapy-radiation therapy. © RSNA, 2018 Online supplemental material is available for this article.