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.

Radiomics analysis from magnetic resonance imaging in predicting the grade of nonfunctioning pancreatic neuroendocrine tumors: a multicenter study.

OBJECTIVES: To explore the potential of radiomics features to predict the histologic grade of nonfunctioning pancreatic neuroendocrine tumor (NF-PNET) patients using non-contrast sequence based on MRI. METHODS: Two hundred twenty-eight patients with NF-PNETs undergoing MRI at 5 centers were retrospectively analyzed. Data from center 1 (n = 115) constituted the training cohort, and data from centers 2-5 (n = 113) constituted the testing cohort. Radiomics features were extracted from T2-weighted images and the apparent diffusion coefficient. The least absolute shrinkage and selection operator was applied to select the most important features and to develop radiomics signatures. The area under receiver operating characteristic curve (AUC) was performed to assess models. RESULTS: Tumor boundary, enhancement homogeneity, and vascular invasion were used to construct the radiological model to stratify NF-PNET patients into grade 1 and 2/3 groups, which yielded AUC of 0.884 and 0.684 in the training and testing groups. A radiomics model including 4 features was constructed, with an AUC of 0.941 and 0.871 in the training and testing cohorts. The fusion model combining the radiomics signature and radiological characteristics showed good performance in the training set (AUC = 0.956) and in the testing set (AUC = 0.864), respectively. CONCLUSION: The developed model that integrates radiomics features with radiological characteristics could be used as a non-invasive, dependable, and accurate tool for the preoperative prediction of grade in NF-PNETs. CLINICAL RELEVANCE STATEMENT: Our study revealed that the fusion model based on a non-contrast MR sequence can be used to predict the histologic grade before operation. The radiomics model may be a new and effective biological marker in NF-PNETs. KEY POINTS: The diagnostic performance of the radiomics model and fusion model was better than that of the model based on clinical information and radiological features in predicting grade 1 and 2/3 of nonfunctioning pancreatic neuroendocrine tumors (NF-PNETs). Good performance of the model in the four external testing cohorts indicated that the radiomics model and fusion model for predicting the grades of NF-PNETs were robust and reliable, indicating the two models could be used in the clinical setting and facilitate the surgeons' decision on risk stratification. The radiomics features were selected from non-contrast T2-weighted images (T2WI) and diffusion-weighted imaging (DWI) sequence, which means that the administration of contrast agent was not needed in grading the NF-PNETs.

Redox Relay-Induced C-S Radical Cross-Coupling Strategy: Application in Nontraditional Site-Selective Thiocyanation of Quinoxalinones.

Oxidative cross-coupling is a powerful strategy to form C-heteroatom bonds. However, oxidative cross-coupling for constructing C-S bond is still a challenge due to sulfur overoxidation and poisoning transition-metal catalysts. Now, electrochemical redox relay using sulfur radicals formed in situ from inorganic sulfur source offers a solution to this problem. Herein, electrochemical redox relay-induced C-S radical cross-coupling of quinoxalinones and ammonium thiocyanate with bromine anion as mediator is presented. The electrochemical redox relay comprised initially the formation of sulfur radical via indirect electrochemical oxidation, simultaneous electrochemical reduction of the imine bond, electro-oxidation-triggered radical coupling involving dearomatization-rearomatization, and the reformation of the imine bond through anodic oxidation. Applying this strategy, various quinoxalinones bearing multifarious electron-deficient/-rich substituents at different positions were well compatible with moderate to excellent yields and good steric hindrance compatibility under constant current conditions in an undivided cell without transition-metal catalysts and additional redox reagents. Synthetic applications of this methodology were demonstrated through gram-scale preparation and follow-up transformation. Notably, such a unique strategy may offer new opportunities for the development of new quinoxalinone-core leads.

Brønsted Acid-Catalyzed Intramolecular Tandem Double Cyclization of γ-Hydroxy Acetylenic Ketones with Alkynes into Naphtho[1,2-b]furan-3-ones.

A metal-free and atom-economic route for the synthesis of naphtho[1,2-b]furan-3-ones has been realized via p-TsOH·H2O-catalyzed intramolecular tandem double cyclization of γ-hydroxy acetylenic ketones with alkynes in formic acid. The benzene-linked furanonyl-ynes are the key intermediates obtained by the scission/recombination of C-O double bonds. Further, the structural modifications of the representative product were implemented by reduction, demethylation, substitution, and [5 + 2]-cycloaddition.

CT Radiomics for Predicting Pathological Complete Response of Axillary Lymph Nodes in Breast Cancer After Neoadjuvant Chemotherapy: A Prospective Study.

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.

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.

Dual Proton/Silver-Catalyzed Serial (5 + 2)-Cycloaddition and Nazarov Cyclization of (E)-2-Arylidene-3-hydroxyindanones with Conjugated Eneynes: Synthesis of Indanone-Fused Benzo[cd]azulenes.

A one-pot step-economic tandem process involving (5 + 2)-cycloaddition and Nazarov cyclization reactions has been reported for the facile synthesis of indanone-fused benzo[cd]azulenes from (E)-2-arylidene-3-hydroxyindanones and conjugated eneynes. This highly regio- and stereoselective bisannulation reaction is enabled by dual silver and Brønsted acid catalysis and opens up a new avenue for the construction of important bicyclo[5.3.0]decane skeletons.

E2EDA: Protein Domain Assembly Based on End-to-End Deep Learning.

With the development of deep learning, almost all single-domain proteins can be predicted at experimental resolution. However, the structure prediction of multi-domain proteins remains a challenge. Achieving end-to-end protein domain assembly and further improving the accuracy of the full-chain modeling by accurately predicting inter-domain orientation while improving the assembly efficiency will provide significant insights into structure-based drug discovery. In this work, we propose an End-to-End Domain Assembly method based on deep learning, named E2EDA. We first develop RMNet, an EfficientNetV2-based deep learning model that fuses multiple features using an attention mechanism to predict inter-domain rigid motion. Then, the predicted rigid motions are transformed into inter-domain spatial transformations to directly assemble the full-chain model. Finally, the scoring strategy RMscore is designed to select the best model from multiple assembled models. The experimental results show that the average TM-score of the model assembled by E2EDA on the benchmark set (282) is 0.827, which is better than those of other domain assembly methods SADA (0.792) and DEMO (0.730). Meanwhile, on our constructed multi-domain data set from AlphaFold DB, the model reassembled by E2EDA is 7.0% higher in TM-score compared to the full-chain model predicted by AlphaFold2, indicating that E2EDA can capture more accurate inter-domain orientations to improve the quality of the model predicted by AlphaFold2. Furthermore, compared to SADA and AlphaFold2, E2EDA reduced the average runtime on the benchmark by 64.7% and 19.2%, respectively, indicating that E2EDA can significantly improve assembly efficiency through an end-to-end approach. The online server is available at http://zhanglab-bioinf.com/E2EDA.

Discrimination of Liver Metastases of Digestive System Neuroendocrine Tumors From Neuroendocrine Carcinoma by Computed Tomography-Based Radiomics Analysis.

OBJECTIVE: The aim of the study is to investigate the value of computed tomography (CT) radiomics features to discriminate the liver metastases (LMs) of digestive system neuroendocrine tumors (NETs) from neuroendocrine carcinoma (NECs). METHODS: Ninety-nine patients with LMs of digestive system neuroendocrine neoplasms from 2 institutions were included. Radiomics features were extracted from the portal venous phase CT images by the Pyradiomics and then selected by using the t test, Pearson correlation analysis, and least absolute shrinkage and selection operator method. The radiomics score (Rad score) for each patient was constructed by linear combination of the selected radiomics features. The radiological model was constructed by radiological features using the multivariable logistic regression. Then, the combined model was constructed by combining Rad score and the radiological model into logistic regression. The performance of all models was evaluated by the receiver operating characteristic curves with the area under curve (AUC). RESULTS: In the radiological model, only the enhancement degree (odds ratio, 8.299; 95% confidence interval, 2.070-32.703; P = 0.003) was an independent predictor for discriminating the LMs of digestive system NETs from those of NECs. The combined model constructed by the Rad score in combination with the enhancement degree showed good discrimination performance, with AUCs of 0.893, 0.841, and 0.740 in the training, testing, and external validation groups, respectively. In addition, it performed better than radiological model in the training and testing groups (AUC, 0.893 vs 0.726; AUC, 0.841 vs 0.621). CONCLUSIONS: The CT radiomics might be useful for discrimination LMs of digestive system NECs from NETs.

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).

MRI measurements predict major low anterior resection syndrome in rectal cancer patients.

PURPOSE: Current low anterior resection syndrome (LARS) score is lagging behind and only based on clinical symptoms patient described. Preoperative imaging indicators which can be used to predict LARS is unknown. We proposed preoperative MRI parameters for identifying major LARS. METHODS: Patients receiving curative restorative anterior resection from Sept. 2007 to Sept. 2015 were collected to complete LARS score (median 75.7 months since surgery). 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: Two hundred fifty-five patients undergoing neoadjuvant chemoradiotherapy and 72 patients undergoing direct surgery were enrolled. The incidence of major LARS in NCRT group was significantly higher (53.3% vs.34.7%, P = 0.005). In patients with neoadjuvant chemoradiotherapy, the thickness of ARJ (TARJ), the distance between the tumor's lower edge and anal rectal joint (DTA), and sex were independent factors for predicting major LARS; ORs were 0.382 (95% CI, 0.198-0.740), 0.653 (95% CI, 0.565-0.756), and 0.935 (95% CI, 0.915-0.955). The AUC of the multivariable model was 0.842 (95% CI, 0.794-0.890). In patients with direct surgery, only DTA was the independent factor for predicting major LARS; OR was 0.958 (95% CI, 0.930-0.988). The AUC was 0.777 (95% CI: 0.630-0.925). CONCLUSIONS: Baseline MRI measurements have the potential to predict major LARS in rectal cancer, which will benefit the decision-making and improve patients' life quality.

Comparison Between Percutaneous Kyphoplasty and Percutaneous Vertebroplasty in Terms of Efficacy in Osteoporotic Vertebral Compression Fractures: A Meta-analysis.

Background and Aim: 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. Methods: 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. Results: 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. Conclusion: 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.

Metal-Free Intramolecular [3+2] Cycloaddition of γ-Hydroxy Acetylenic Ketones with Alkynes for the Synthesis of Naphtho[1,2-c]furan-5-ones and Its Derivatization via a Selective C(sp2)-H Deuteration Reaction.

A metal-free intramolecular [3+2] cycloaddtion has been achieved by treating benzene-linked propynol-ynes with AcOH/H2O in a one-pot manner. The reaction provides greener, 100% atom-economic, highly regioselective, and more practical access to functionalized naphtho[1,2-c]furan-5-ones with valuable and versatile applications. The regioselective α-deuteration of naphtho[1,2-c]furan-5-ones has been also presented with excellent deuterium incorporation and chemical yields. Moreover, the fluorescent properties of naphtho[1,2-c]furan-5-one products have been investigated in solution.

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.

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.

Use of Radiomics to Predict Response to Immunotherapy of Malignant Tumors of the Digestive System.

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.

Whole lesion histogram analysis of apparent diffusion coefficients on MRI predicts disease-free survival in locally advanced squamous cell cervical cancer after radical chemo-radiotherapy.

BACKGROUND: The aim was to investigate the prognostic value of MR apparent diffusion coefficients (ADC) using histogram analysis (HA) in predicting disease-free survival (DFS) of cervical cancer after chemo-radiation therapy. METHODS: We retrospectively analyzed 103 women with pathologically proven squamous cell uterine cancer who received chemo-radiation therapy between 2009 and 2013. All patients were followed up for more than 2 years. Pre-treatment MR images were retrieved and imported for HA using an in-house developed software program based on 3D Slicer. Regions of interest of whole tumors were drawn manually on DWI with reference to T2WI. HA features (mean, max, min, 50, 10, 90%, kurtosis, and skewness) were extracted from apparent diffusion coefficient (ADC) maps and compared between the recurrence and non-recurrence groups after the 2-year follow-up. Univariate and multivariate Cox regression analysis was used to correlate ADC HA features and relevant clinical variables (age, grade, maximal diameter of tumor, FIGO stage, SCC-Ag) with DFS. RESULTS: One hundred three patients with stage IB-IV cervical cancers were followed up for 2.0-94.6 months (median 48.9 months). Twenty patients developed recurrence within 2 years. In the recurrence group, the min (P = 0.001) and 10% (P = 0.048) ADC values were significantly lower than those of the non-recurrence group. Univariate and multivariate Cox regression analysis revealed that ADCmin (P = 0.006, HR = 0.110) was significantly correlated with DFS. CONCLUSION: Pre-treatment volumetric ADCmin in histogram analysis is an independent factor that is correlated with DFS in cervical cancer patients treated with chemo-radiation therapy.

Brønsted-Acid-Catalyzed Synthesis of 3-Alkoxy and 3-Sulfamido Indanones via a Tandem Cyclization.

Brønsted-acid-catalyzed allylic substitution reactions of the in situ generated 3-hydroxy indanones with alcohols and sulfamides were investigated, which provided a facile route for the synthesis of a large variety of 3-alkoxy and 3-sulfamido indanones. The key intermediates, 3-hydroxy indanones, were obtained through the intramolecular Meyer-Schuster rearrangement of o-propargyl alcohol benzaldehydes. The resulting 3-benzyloxy indanone could be selectively modified by allylic sulfonamidation and reduction reactions.