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Background The diagnostic effectiveness of traditional imaging techniques is insufficient to assess the response of lymph nodes (LNs) to neoadjuvant chemotherapy (NAC), especially for pathological complete response (pCR). A radiomics model based on computed tomography (CT) could be helpful. Patients and Methods Prospective consecutive breast cancer patients with positive axillary LNs initially were enrolled, who received NAC prior to surgery. Chest contrast-enhanced thin-slice CT scan was performed both before and after the NAC (recorded as the first and the second CT respectively), and on both of them, the target metastatic axillary LN was identified and demarcated layer by layer. Using pyradiomics-based software that was independently created, radiomics features were retrieved. A pairwise machine learning workflow based on Sklearn (https://scikit-learn.org/) and FeAture Explorer was created to increase diagnostic effectiveness. An effective pairwise auto encoder model was developed by the improvement of data normalization, dimensionality reduction, and features screening scheme as well as the comparison of the prediction effectiveness of the various classifiers, Results A total of 138 patients were enrolled, and 77 (58.7%) in the overall group achieved pCR of LN after NAC. Nine radiomics features were finally chosen for modeling. The AUCs of the training group, validation group, and test group were 0.944 (0.919-0.965), 0.962 (0.937-0.985), and 1.000 (1.000-1.000), respectively, and the corresponding accuracies were 0.891, 0.912, and 1.000. Conclusion The pCR of axillary LNs in breast cancer following NAC can be precisely predicted using thin-sliced enhanced chest CT-based radiomics.

As a data-driven design method, model-free optimal control based on reinforcement learning provides an effective way to find optimal control strategies. The design of model-free optimal control is sensitive to system data because it relies on data rather than detailed dynamic models. A prerequisite for generating applicable data is that the system must be open-loop stable (with a stable equilibrium point), which restricts the data-based control design methods in actual control problems and leads to rare experimental studies or verification in the literature. To improve this situation and enrich its applications, we propose a pre-stabilized mechanism and apply it to the motion control of a mechanical system together with a reinforcement learning-based model-free optimal control method, which constitutes a so-called hierarchical control structure. We design two real-time control experiments on an underactuated system to verify its effectiveness. The control results show that the proposed hierarchical control is quite promising in controlling this mechanical system, even though it is open-loop unstable with unknown dynamics.

The cT3 substage criteria based on extramural depth of tumor invasion in rectal cancer have several limitations.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.This is a cohort study.This study included a prospective, single-center, observational cohort and a retrospective, multicenter, independent validation cohort.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.Baseline MRI with the distance between the deepest tumor invasion and mesorectal fascia, extramural depth of tumor invasion, and mesorectum thickness were measured.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.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.The sample size is relatively small, and the study focused on cT3 rectal cancers with a negative mesorectal fascia.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).

Purpose: To develop and validate a radiomics model for evaluating pathologic complete response (pCR) to neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer (LARC).Experimental Design: We enrolled 222 patients (152 in the primary cohort and 70 in the validation cohort) with clinicopathologically confirmed LARC who received chemoradiotherapy before surgery. All patients underwent T2-weighted and diffusion-weighted imaging before and after chemoradiotherapy; 2,252 radiomic features were extracted from each patient before and after treatment imaging. The two-sample t test and the least absolute shrinkage and selection operator regression were used for feature selection, whereupon a radiomics signature was built with support vector machines. Multivariable logistic regression analysis was then used to develop a radiomics model incorporating the radiomics signature and independent clinicopathologic risk factors. The performance of the radiomics model was assessed by its calibration, discrimination, and clinical usefulness with independent validation.Results: The radiomics signature comprised 30 selected features and showed good discrimination performance in both the primary and validation cohorts. The individualized radiomics model, which incorporated the radiomics signature and tumor length, also showed good discrimination, with an area under the receiver operating characteristic curve of 0.9756 (95% confidence interval, 0.9185–0.9711) in the validation cohort, and good calibration. Decision curve analysis confirmed the clinical utility of the radiomics model.Conclusions: Using pre- and posttreatment MRI data, we developed a radiomics model with excellent performance for individualized, noninvasive prediction of pCR. This model may be used to identify LARC patients who can omit surgery after chemoradiotherapy. Clin Cancer Res; 23(23); 7253–62. ©2017 AACR.

Table S1. Demographic comparison between Primary and Validation cohorts; Table S2. Radiomics features associated with CRT outcome identified by two sample t-test and LASSO; Table S3. Related factors for pCR detection in LARC; Table S4. Statistical significance of the clinical information in the multivariate regression model; Figure S1. Recruitment pathway for patients in this study; Figure S2. Typical cases of pCR and non-pCR.

The novel C2–O sulfonylation of quinoxalinones via electro-oxidation induced O–S coupling strategy under mild conditions was reported.

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.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.Retrospective.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).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/mmDWI 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.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.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 DThe proposed signature-like pictures provide more accurate pretreatment prediction of the response to neoadjuvant chemoradiotherapy than the fitted methods for locally advanced rectal cancer.3 TECHNICAL EFFICACY: Stage 2.

Purpose To assess the MRI performance in differentiating pancreatic ductal adenocarcinomas (PDACs), from solid pseudopapillary neoplasms (SPNs) and pancreatic neuroendocrine tumors (PNETs) using non-gaussian diffusion-weighted imaging models. Methods This was a retrospective study of patients diagnosed with PDACs (01/2015–06/2019) or with PNETs or SPNs diagnosed (01/2011–12/2019) at our hospital. The lesions were randomized 1:1 to the primary and validation cohorts. The regions of interest (ROIs) were manually drawn on each slice at DWI (b = 1500 s/mm2) from 3 T MRI. D (diffusion coefficient), D* (pseudodiffusion coefficient), f (perfusion fraction), distributed diffusion coefficient (DDC), α (diffusion heterogeneity index), mean diffusivity (MD) and mean kurtosis (MK) were obtained. The parameters with largest performance for differentiation were used to establish a diagnostic model. Results There were 148, 56, and 60 patients with PDAC, PNET, and SPN, respectively. For differentiating PDACs from SPNs, f and MK values were used to establish a diagnostic model with areas under the receiver operating characteristic curves (AUCs) of 0.92 and 0.89 in the primary and validation groups, respectively. For distinguishing PDACs from PNETs, α and MK values were used to establish a diagnostic model with AUCs of 0.87 and 0.86 in the primary and validation groups, respectively. The accuracy rate of the subjective evaluation with the assistance of non-gaussian DWI models for differentiating PDAC from SPNs and PNETs were higher than that of subjective diagnosis alone (P Conclusions The non-gaussian DWI models could assist radiologists in accurately differentiating PDACs from PNETs and SPNs.

Objective. The diagnosis of primary malignant melanoma of the esophagus (PMME) before treatment is essential for clinical decision-making. However, PMME may be misdiagnosed as esophageal squamous cell carcinoma (ESCC) sometimes. This research is aimed at devising a radiomics nomogram model of CT for distinguishing PMME from ESCC. Methods. In this retrospective analysis, 122 individuals with proven pathologically PMME ( n = 28 ) and ESCC ( n = 94 ) were registered from our hospital. PyRadiomics was applied to derive radiomics features from plain and enhanced CT images after resampling image into an isotropic resolution of 0.625 × 0.625 × 0.625 m m 3 . The diagnostic efficiency of the model was evaluated by an independent validation group. Results. For the purpose of differentiation between PMME and ESCC, a radiomics model was constructed using 5 radiomics features obtained from nonenhanced CT and 4 radiomics features derived from enhanced CT. A radiomics model including multiple radiomics features showed excellent discrimination efficiency with AUCs of 0.975 and 0.906 in the primary and validation cohorts, respectively. Then, a radiomics nomogram model was developed. The decision curve analysis has shown remarkable performance of this nomogram model for distinguishing PMME from ESCC. Conclusions. The proposed radiomics nomogram model based on CT could be used for distinguishing PMME from ESCC. Moreover, this model also contributed to helping clinicians determine an appropriate treatment strategy for esophageal neoplasms.

BACKGROUND Although acute graft-vs-host disease (aGvHD) is a rare complication of liver transplantation, it is poorly understood and has an extremely high mortality rate. No standardized diagnostic criteria or treatment regimens currently exist. CASE SUMMARY The present study investigated the etiology, diagnosis, and treatment of aGvHD following liver transplantation. Presentation, diagnosis, disease course, histology, and treatment of an aGvHD case are reported, and associated literature is reviewed. A 64-year-old female required LTx due to primary biliary cirrhosis. The donor was a 12-year-old male. Three weeks following liver transplantation, the recipient developed pyrexia, diarrhea, rashes, and antibiotic-unresponsive pancytopenia. Clinical symptoms together with laboratory investigations suggested a diagnosis of aGvHD, which was confirmed via peripheral blood fluorescent in situ hybridization. Donor XY chromosome fluorescent in situ hybridization indicating early chimerism achieved 93% sensitivity in the detection of GvHD. Existing immunosuppressants were discontinued, and high-dose intravenous methylprednisolone was initiated along with antibiotics. While diarrhea resolved, the patient’s general condition continued to deteriorate until demise due to multi-system organ failure at 37 d post-liver transplantation. This case illustrates the life-threatening nature of aGvHD. CONCLUSION Herein, we have summarized a post-LTx aGvHD case and reviewed associated literature in order to increase awareness and provide potentially risk-mitigating recommendations.

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.

Osteoporotic vertebral compression fractures (OVCFs) are acknowledged to be common fractures, especially in the elderly population. Minimally invasive percutaneous methods of treatment for these fractures such as kyphoplasty (KP) and vertebroplasty (VP) have been valid and effective tools for decreasing clinical problems, which are associated with more beneficial effects compared with traditional methods such as open surgery or conservative treatment. Hence, we conducted the current meta-analysis in order to gather updated evidence for the systematic assessment of clinical and radiographic outcomes of KP compared with VP.We searched articles published based on the electronic databases, including PubMed, EMBASE, and Cochrane Library. Publications of studies comparing KP with VP in the treatment of OVCFs were collected. After rigorous and thorough review of study quality, we extracted the data on the basis of eligible trials, which analyzed the summary hazard ratios (HRs) of the end points of interest.Our inclusion criteria involved a total of 6 studies. In total, data from 644 patients, 330 who received VP and 284 who received KP, were included in the review. There was no significant difference in either group in terms of visual analog scale (VAS) scores (MD = 0.17; 95% CI, -0.39 to 0.73; P = .56), risk of cement leakage (odds ratio [OR] = 1.31; 95% CI, 0.62 to 2.74; P = .47) or Oswestry Disability Index (ODI) scores (MD = 0.51; 95% CI, -1.87 to 2.88; P = .68). Nevertheless, the injected cement volume (MD = -0.52; 95% CI, -0.88 to -0.15; P = .005) in the VP group was linked to a markedly lower statistically significant trend compared with the KP group.This meta-analysis evaluated acceptable efficacy levels across the involved trials. VP injected cement volume had several advantages in this meta-analysis. Yet, no significant differences were observed in terms of VAS scores, ODI scores, or cement leakage when KP was compared to VP therapy. Given the combined results of our study, the optimal treatment for patients with OVCFs should be determined by further high-quality multicenter randomized controlled trials with longer follow-up and larger sample sizes.

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.

Background The aim of the study was to develop a deep learning (DL) algorithm to evaluate the pathological complete response (pCR) to neoadjuvant chemotherapy in breast cancer. Methods A total of 302 breast cancer patients in this retrospective study were randomly divided into a training set (n = 244) and a validation set (n = 58). Tumor regions were manually delineated on each slice by two expert radiologists on enhanced T1‐weighted images. Pathological results were used as ground truth. Deep learning network contained five repetitions of convolution and max‐pooling layers and ended with three dense layers. The pre‐NAC model and post‐NAC model inputted six phases of pre‐NAC and post‐NAC images, respectively. The combined model used 12 channels from six phases of pre‐NAC and six phases of post‐NAC images. All models above included three indexes of molecular type as one additional input channel. Results The training set contained 137 non‐pCR and 107 pCR participants. The validation set contained 33 non‐pCR and 25 pCR participants. The area under the receiver operating characteristic (ROC) curve (AUC) of three models was 0.553 for pre‐NAC, 0.968 for post‐NAC and 0.970 for the combined data, respectively. A significant difference was found in AUC between using pre‐NAC data alone and combined data (P

Purpose: Current low anterior resection syndrome (LARS) score is lagging behind and only based on clinical symptoms patient described. Whether there are preoperative imaging indicators can be used to predict LARS is unknown. We proposed to identify patients with high risk for major LARS by MRI measurements.Methods: Patients received curative restorative anterior resection (median 75.7 months since surgery) from Sept. 2007 to Sept. 2015 were collected to complete LARS score. MRI measurements associated with LARS were tested, and a multivariate logistic model was conducted for predicting LARS. Receiver operating characteristic curve was used to evaluate the model.Results: 255 patients (neoadjuvant chemoradiotherapy) and 72 patients (direct surgery) were enrolled. The incidence of major LARS in NCRT group was significantly higher (53.3% vs.34.7%) (P＝0.005).This study showed that the distance between the tumor’s lower edge and anal rectal joint (ARJ)(DTA) was an important factor for major LARS in both groups (PConclusions: Baseline MRI measurements have the potential to predict major LARS in rectal cancer, which will benefit the decision making and improve patients’ life quality.

To assess the value of radiomics, apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM) and stretched-exponential (SE) MR imaging in prediction of therapeutic response in patients with spinal metastases before chemotherapy.Thirty-six patients with 190 osteolytic metastatic lesions from breast cancer were prospectively enrolled and underwent MR imaging before and after 6 months' treatment on a 1.5 T MRI. According to MDA criteria, 68 lesions were categorized as progressive disease (PD) and 122 lesions were categorized as stable or improvement (non-PD). The regions of interest (ROIs) were manually drawn on DWI, T1WI, T2WI and FS-T2WI by two radiologists with ITK-SNAP. The ADCThe mean values of ADC, DDC, and D before treatment were significantly higher in non-PD group than those in PD group (P = 0.001). The radiomics based on ADCRadiomics model based on ADC

The use of endoscopic surgery for treating gastrointestinal stromal tumors (GISTs) between 2 and 5 cm remains controversial considering the potential risk of metastasis and recurrence. Also, surgeons are facing great difficulties and challenges in assessing the malignant potential of 2-5 cm gastric GISTs.To develop and evaluate computerized tomography (CT)-based radiomics for predicting the malignant potential of primary 2-5 cm gastric GISTs.A total of 103 patients with pathologically confirmed gastric GISTs between 2 and 5 cm were enrolled. The malignant potential was categorized into low grade and high grade according to postoperative pathology results. Preoperative CT images were reviewed by two radiologists. A radiological model was constructed by CT findings and clinical characteristics using logistic regression. Radiomic features were extracted from preoperative contrast-enhanced CT images in the arterial phase. The XGboost method was used to construct a radiomics model for the prediction of malignant potential. Nomogram was established by combing the radiomics score with CT findings. All of the models were developed in a training group (The area under the curve (AUC) value of the radiological, radiomics, and nomogram models was 0.753 (95% confidence interval [CI]: 0.597-0.909), 0.919 (95%CI: 0.828-1.000), and 0.916 (95%CI: 0.801-1.000) in the training groupRadiomics may be an effective tool to predict the malignant potential of 2-5 cm gastric GISTs and assist preoperative clinical decision making.

To explore the value of parameters of the on hepatobiliary phase (HBP) of pre-treatment gadoxetic acid-enhanced magnetic resonance imaging (MRI) for predicting pathological response to systemic therapy in colorectal liver metastases (CRLMs), compared with response evaluation criteria in solid tumors, version 1.1 (RECIST 1.1).A total of 96 patients with CRLMs who underwent gadoxetic acid-enhanced MRI prior to treatment and then liver resection from January 2017 to December 2021 were enrolled. The pathological response was assessed by the percentage of residual tumors (RTs), and CRLMs were classified into two groups according to the pathological response grade (PRG): (1) strong response (including PRG2 and PRG3, RTs ≤ 10%), and weak response (PRG1, RTs 10%). Two radiologists evaluated the enhancement pattern and degree of CRLMs on the HBP. The diameter, mean and standard deviation (SD) value of signal intensity (SI) of CRLMs on pre-contrast and HBP images were recorded. Relative tumor enhancement (RTE) and the SD ratio (SDR) were calculated. These parameters were analyzed in terms of pathological response on a lesion-by-lesion basis.Totally, 263 CRLMs were classified into: the strong response group (PRG2, n = 57; PRG3, n = 7) and the weak response group (PRG1, n = 199). RTE and SDR values were significantly higher in the strong response group than in the weak response group (P 0.001). RTE values (P 0.001) and SDR values (P = 0.031) were independent factors for predicting strong response. The area under curve (AUC) of RTE and SDR values were 0.725 and 0.652, respectively. The combination of these parameters was 0.750, which performed better than RECIST 1.1 (0.750 vs 0.531; P 0.001).RTE and SDR values on HBP are potential features in predicting pathological response to systemic therapy in CRLMs.

Purpose To evaluate the potential role of MR findings and DWI parameters in predicting small regional lymph nodes metastases (with short-axis diameter Methods A total of 127 patients, 82 in training group and 45 in testing group, with histopathologically diagnosed PDACs who underwent pancreatectomy were retrospectively analyzed. PDACs were divided into two groups of positive and negative lymph node metastases (LNM) based on the pathological results. Pancreatic cancer characteristics, short axis of largest lymph node, and DWI parameters of PDACs were evaluated. Results Univariate and multivariate analyses showed that extrapancreatic distance of tumor invasion, short-axis diameter of the largest lymph node, and mean diffusivity of tumor were independently associated with small LNM in patients with PDACs. The combining MRI diagnostic model yielded AUCs of 0.836 and 0.873, and accuracies of 81.7% and 80% in the training and testing groups. The AUC of the MRI model for predicting LNM was higher than that of subjective MRI diagnosis in the training group (rater 1, P = 0.01; rater 2, 0.008) and in a testing group (rater 1, P = 0.036; rater 2, 0.024). Comparing the subjective diagnosis, the error rate of the MRI model was decreased. The defined LNM-positive group by the MRI model showed significantly inferior overall survival compared to the negative group (P = 0.006). Conclusions The MRI model showed excellent performance for individualized and noninvasive prediction of small regional LNM in PDACs. It may be used to identify PDACs with small LNM and contribute to determining an appropriate treatment strategy for PDACs.

The one-pot synthesis of dibenzo[a,f]azulen-12-ones has been established starting from o-propargyl alcohol benzaldehydes and alkynes. The key azulenone bicyclic skeletons were formed through the intramolecular tandem cyclization and intermolecular [5 + 2]-cycloaddition sequence. This annulation process showed high atom and step economy, regioselectivity and stereoselectivity by creating three C–C and one C–O bonds.

An experimental investigation was conducted for flow boiling inside three horizontal stainless-steel tubes with a series of newly-developed three-dimensional enhanced surfaces. Both the inner and outer surfaces of the 1EHT tubes were composed of dimples in different sizes and depths, and with an array of rectangular cavities for the 3EHT tube. These enhanced tubes have the same outer and inner diameters of 9.52 mm and of 8.32 mm, respectively. The evaporation experiments were performed using R410A as the working fluid at a constant saturation temperature of 279 K. The mass fluxes varied from 160 to 390 kg/m2s, with inlet and outlet vapor qualities of 0.2 and 0.8, respectively. As the mass velocity increased, the heat transfer coefficient and pressure drop penalty increased accordingly. Experimental results showed that the pressure drop of the 1EHT-1 tube is larger than those of the 1EHT-2 and 3EHT tubes, while the 1EHT-2 tube has the highest evaporation heat transfer coefficient.

A metal-free intramolecular [3+2] cycloaddtion has been achieved by treating benzene-linked propynol-ynes with AcOH/H

BACKGROUND Despite the promising results of immunotherapy in cancer treatment, new response patterns, including pseudoprogression and hyperprogression, have been observed. Radiomics is the automated extraction of high-fidelity, high-dimensional imaging features from standard medical images, allowing comprehensive visualization and characterization of the tissue of interest and corresponding microenvironment. This study assessed whether radiomics can predict response to immunotherapy in patients with malignant tumors of the digestive system. MATERIAL AND METHODS Computed tomography (CT) images of patients with malignant tumors of the digestive system obtained at baseline and after immunotherapy were subjected to radiomics analyses. Radiomics features were extracted from each image. The formula of the screened features and the final predictive model were obtained using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. RESULTS Imaging analysis was feasible in 87 patients, including 3 with pseudoprogression and 7 with hyperprogression. One hundred ten radiomics features were obtained before and after treatment, including 109 features of the target lesions and 1 of the aorta. Four models were constructed, with the model constructed from baseline and post-treatment CT features having the best classification performance, with a sensitivity, specificity, and AUC of 83.3%, 88.9%, and 0.806, respectively. CONCLUSIONS Radiomics can predict the response of patients with malignant tumors of the digestive system to immunotherapy and can supplement conventional evaluations of response.

Background and purpose Pretreatment prediction of the response to neoadjuvant chemoradiotherapy (NCRT) helps to determine the subsequent plans for the patients with locally advanced rectal cancer (LARC). If the good responders (GR) and non-good responders (non-GR) can be accurately predicted, they can choose to intensify the neoadjuvant chemoradiotherapy to decrease the risk of tumor progression during NCRT and increase the chance of organ preservation. Compared with radiomics methods, deep learning (DL) may adaptively extract features from the images without the need of feature definition. However, DL suffers from limited training samples and signal discrepancy among different scanners. This study aims to construct a DL model to predict GRs by training apparent diffusion coefficient (ADC) images from different scanners. Methods The study retrospectively recruited 700 participants, chronologically divided into a training group (n = 500) and a test group (n = 200). Deep convolutional neural networks were constructed to classify GRs and non-GRs. The networks were designed with a max-pooling layer parallelized by a center-cropping layer to extract features from both the macro and micro scale. ADC images and T2-weighted images were collected at 1.5 Tesla and 3.0 Tesla. The networks were trained by the image patches delineated by radiologists in ADC images and T2-weighted images, respectively. Pathological results were used as the ground truth. The deep learning models were evaluated on the test group and compared with the prediction by mean ADC value. Results Area under curve (AUC) of receiver operating characteristic (ROC) is 0.851 (95% CI: 0.789-0.914) for DL model with ADC images (DL_ADC), significantly larger (P = 0.018, Z = 2.367) than that of mean ADC with AUC = 0.723 (95% CI: 0.637-0.809). The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of DL_ADC model are 94.3%, 68.3%, 87.4% and 83.7%, respectively. The DL model with T2-weighted images (DL_T2) produces an AUC of 0.721 (95% CI: 0.640-0.802), significantly (P = 0.000, Z = 3.554) lower than that of DL_ADC model. Conclusion Deep learning model reveals the potential of pretreatment apparent diffusion coefficient images for the prediction of good responders to neoadjuvant chemoradiotherapy.

Objective To develop and validate a radiomics model of diffusion kurtosis imaging (DKI) and T2 weighted imaging for discriminating pancreatic neuroendocrine tumors (PNETs) from solid pseudopapillary tumors (SPTs). Materials and Methods Sixty-six patients with histopathological confirmed PNETs (n = 31) and SPTs (n = 35) were enrolled in this study. ROIs of tumors were manually drawn on each slice at T2WI and DWI (b = 1,500 s/mm2) from 3T MRI. Intraclass correlation coefficients were used to evaluate the interobserver agreement. Mean diffusivity (MD) and mean kurtosis (MK) were derived from DKI. The least absolute shrinkage and selection operator regression were used for feature selection. Results MD and MK had a moderate diagnostic performancewith the area under curve (AUC) of 0.71 and 0.65, respectively. A radiomics model, which incorporated sex and age of patients and radiomics signature of the tumor, showed excellent discrimination performance with AUC of 0.97 and 0.86 in the primary and validation cohort. Moreover, the new model had better diagnostic performance than that of MD (P = 0.023) and MK (P = 0.004), and showed excellent differentiation with a sensitivity of 95.00% and specificity of 91.67% in primary cohort, and the sensitivity of 90.91% and specificity of 81.82% in the validation cohort. The accuracy of radiomics analysis, radiologist 1, and radiologist 2 for diagnosing SPTs and PNETs were 92.42, 77.27, and 78.79%, respectively. The accuracy of radiomics analysis was significantly higher than that of subjective diagnosis (P < 0.05). Conclusions Radiomics model could improve the diagnostic accuracy of SPTs and PNETs and contribute to determining an appropriate treatment strategy for pancreatic tumors.

Objective: Determine the performance of a computed tomography (CT) -based radiomics model in predicting early response to immunotherapy in patients with metastatic melanoma. Methods: This retrospective study examined 50 patients with metastatic melanoma who received immunotherapy treatment in our hospital with an anti-programmed cell death-1 (PD-1) agent or an inhibitor of cytotoxic T lymphocyte antigen-4 (CTLA-4). Thirty-four patients who received an anti-PD-1 agent were in the training sample and 16 patients who received a CTLA-4 inhibitor were in the validation sample. Patients with true progressive disease (PD) were in the poor response group, and those with pseudoprogression, complete response (CR), partial response (PR), or stable disease (SD) were in the good response group. CT images were examined at baseline and after the first and second cycles of treatment, and the imaging data were extracted for radiomics modeling. Results: The radiomics model based on pre-treatment, post-treatment, and delta features provided the best results for predicting response to immunotherapy. Receiver operating characteristic (ROC) analysis for good response indicated an area under the curve (AUC) of 0.882 for the training group and an AUC of 0.857 for the validation group. The sensitivity, specificity, and accuracy of model were 85.70% (6/7), 66.70% (6/9), and 75% (12/16) for predicting a good response. Conclusion: A CT-based radiomics model for metastatic melanoma has the potential to predict early response to immunotherapy and to identify pseudoprogression.

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.

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

Background Gastrointestinal stromal tumors (GISTs) account for less than 1% of all gastrointestinal tumors. The biological behaviors of GISTs vary from benign to malignant. GISTs are common in the stomach (55.6%) and small intestine (31.8%), but rarely in the rectum, colon (6%), and other sites (5.5%). Currently, the majority of published reports of primary appendiceal stromal tumors (PASTs) are case reports or case series. Methods The PASTs described in this study were identified from a literature review (23 cases) and our center (one case). The relationship between PAST gross types and clinicopathological factors was analyzed and summarized. At the same time, the study also analyzed the related risk factors and survival of PASTs and GISTs. Results Twenty-four cases of PASTs were compared with 254 cases of GISTs from our center. The results showed that there was a significant difference between the two groups in tumor size (P

Post‑translational modification via small ubiquitin‑like modifier (SUMO) is involved in the regulation of various important cellular processes. SUMO modification can be regulated at the level of conjugation, and can also be reversed by the SUMO‑specific proteases (SENPs). However, current studies of the regulation and function of SENP in lung development remain limited. In this study, the expression levels of SENP1 and SUMO1 were assessed during lung development in rats. SUMO1 modification occurred during lung development and changes in SENP1 expression were consistent with the changes in the presence of free SUMO1. In order to investigate the function of SENP1, alveolar type (AT) 2 cells were transfected with SENP1‑targeting small interfering RNA, and the proliferation, apoptosis and differentiation function of AT2 cells was subsequently evaluated. Marked upregulation of conjugated SUMO1 was observed following SENP1 inhibition. Furthermore, depletion of SENP1 resulted in increased apoptosis, decreased proliferation and impaired differentiation status of AT2 cells. Thus, the results support that SENP1 is an essential regulator of the balance between SUMOylation and deSUMOylation during lung development, specifically affecting the proliferation and differentiation status of AT2 cells.

It is well known that the Reynolds number has a significant effect on the vortex-induced vibrations (VIV) of cylinders. In this paper, a novel in-line (IL) and cross-flow (CF) coupling VIV prediction model for circular cylinders has been proposed, in which the influence of the Reynolds number was comprehensively considered. The Strouhal number linked with the vortex shedding frequency was calculated through a function of the Reynolds number. The coefficient of the mean drag force was fitted as a new piecewise function of the Reynolds number, and its amplification resulted from the CF VIV was also taken into account. The oscillating drag and lift forces were modelled with classical van der Pol wake oscillators and their empirical parameters were determined based on the lock-in boundaries and the peak-amplitude formulas. A new peak-amplitude formula for the IL VIV was developed under the resonance condition with respect to the mass-damping ratio and the Reynolds number. When compared with the results from the experiments and some other prediction models, the present model could give good estimations on the vibration amplitudes and frequencies of the VIV both for elastically-mounted rigid and long flexible cylinders. The present model considering the influence of the Reynolds number could generally provide better results than that neglecting the effect of the Reynolds number.

This paper presents probabilistic capacity models for composite floor systems subjected to column loss. The probabilistic capacity models are formulated by adding explanatory terms to an existing d...

An effective and concise electrophilic cyclization of 1,4-butynediols, 4-aminobut-2-yn-1-ols, pent-2-yne-1,5-diols, 2-propynolphenols and 2-propynolanilines with N-halo succinimides and trimethylsilyl halides opens a one-pot synthetic access to five- and six-membered dihalogenated heterocycles in moderate to good yields. The optimized cyclization conditions are tolerated with many functional groups.

An expedient strategy for the preparation of diiodinated diepoxydibenzo[c,k][1,9]dioxacyclohexadecines from readily available 2-(4-hydroxybut-1-yn-1-yl)benzaldehydes through electrophile-triggered tandem cyclization/intermolecular acetalation sequence has been presented. The electrophilic macrocyclization can be performed under mild conditions and in up to gram quantities. Moreover, palladium-catalyzed coupling and reduction reactions of the resulting iodides could efficiently afford oxa-macrocycles.