A Colorectal Coordinate-Driven Method for Colorectum and Colorectal Cancer Segmentation in Conventional CT Scans.

Automated colorectal cancer (CRC) segmentation in medical imaging is the key to achieving automation of CRC detection, staging, and treatment response monitoring. Compared with magnetic resonance imaging (MRI) and computed tomography colonography (CTC), conventional computed tomography (CT) has enormous potential because of its broad implementation, superiority for the hollow viscera (colon), and convenience without needing bowel preparation. However, the segmentation of CRC in conventional CT is more challenging due to the difficulties presenting with the unprepared bowel, such as distinguishing the colorectum from other structures with similar appearance and distinguishing the CRC from the contents of the colorectum. To tackle these challenges, we introduce DeepCRC-SL, the first automated segmentation algorithm for CRC and colorectum in conventional contrast-enhanced CT scans. We propose a topology-aware deep learning-based approach, which builds a novel 1-D colorectal coordinate system and encodes each voxel of the colorectum with a relative position along the coordinate system. We then induce an auxiliary regression task to predict the colorectal coordinate value of each voxel, aiming to integrate global topology into the segmentation network and thus improve the colorectum's continuity. Self-attention layers are utilized to capture global contexts for the coordinate regression task and enhance the ability to differentiate CRC and colorectum tissues. Moreover, a coordinate-driven self-learning (SL) strategy is introduced to leverage a large amount of unlabeled data to improve segmentation performance. We validate the proposed approach on a dataset including 227 labeled and 585 unlabeled CRC cases by fivefold cross-validation. Experimental results demonstrate that our method outperforms some recent related segmentation methods and achieves the segmentation accuracy in DSC for CRC of 0.669 and colorectum of 0.892, reaching to the performance (at 0.639 and 0.890, respectively) of a medical resident with two years of specialized CRC imaging fellowship.

Large-scale pancreatic cancer detection via non-contrast CT and deep learning.

Pancreatic ductal adenocarcinoma (PDAC), the most deadly solid malignancy, is typically detected late and at an inoperable stage. Early or incidental detection is associated with prolonged survival, but screening asymptomatic individuals for PDAC using a single test remains unfeasible due to the low prevalence and potential harms of false positives. Non-contrast computed tomography (CT), routinely performed for clinical indications, offers the potential for large-scale screening, however, identification of PDAC using non-contrast CT has long been considered impossible. Here, we develop a deep learning approach, pancreatic cancer detection with artificial intelligence (PANDA), that can detect and classify pancreatic lesions with high accuracy via non-contrast CT. PANDA is trained on a dataset of 3,208 patients from a single center. PANDA achieves an area under the receiver operating characteristic curve (AUC) of 0.986-0.996 for lesion detection in a multicenter validation involving 6,239 patients across 10 centers, outperforms the mean radiologist performance by 34.1% in sensitivity and 6.3% in specificity for PDAC identification, and achieves a sensitivity of 92.9% and specificity of 99.9% for lesion detection in a real-world multi-scenario validation consisting of 20,530 consecutive patients. Notably, PANDA utilized with non-contrast CT shows non-inferiority to radiology reports (using contrast-enhanced CT) in the differentiation of common pancreatic lesion subtypes. PANDA could potentially serve as a new tool for large-scale pancreatic cancer screening.

How do people perceive different advice for rotator cuff disease? A content analysis of qualitative data collected in a randomised experiment.

OBJECTIVES: To explore how people perceive different advice for rotator cuff disease in terms of words/feelings evoked by the advice and treatment needs. SETTING: We performed a content analysis of qualitative data collected in a randomised experiment. PARTICIPANTS: 2028 people with shoulder pain read a vignette describing someone with rotator cuff disease and were randomised to: bursitis label plus guideline-based advice, bursitis label plus treatment recommendation, rotator cuff tear label plus guideline-based advice and rotator cuff tear label plus treatment recommendation. Guideline-based advice included encouragement to stay active and positive prognostic information. Treatment recommendation emphasised that treatment is needed for recovery. PRIMARY AND SECONDARY OUTCOMES: Participants answered questions about: (1) words/feelings evoked by the advice; (2) treatments they feel are needed. Two researchers developed coding frameworks to analyse responses. RESULTS: 1981 (97% of 2039 randomised) responses for each question were analysed. Guideline-based advice (vs treatment recommendation) more often elicited words/feelings of reassurance, having a minor issue, trust in expertise and feeling dismissed, and treatment needs of rest, activity modification, medication, wait and see, exercise and normal movements. Treatment recommendation (vs guideline-based advice) more often elicited words/feelings of needing treatment/investigation, psychological distress and having a serious issue, and treatment needs of injections, surgery, investigations, and to see a doctor. CONCLUSIONS: Words/feelings evoked by advice for rotator cuff disease and perceived treatment needs may explain why guideline-based advice reduces perceived need for unnecessary care compared to a treatment recommendation.

Deep Learning for Fully Automated Prediction of Overall Survival in Patients Undergoing Resection for Pancreatic Cancer: A Retrospective Multicenter Study.

OBJECTIVE: To develop an imaging-derived biomarker for prediction of overall survival (OS) of pancreatic cancer by analyzing preoperative multiphase contrast-enhanced computed topography (CECT) using deep learning. BACKGROUND: Exploiting prognostic biomarkers for guiding neoadjuvant and adjuvant treatment decisions may potentially improve outcomes in patients with resectable pancreatic cancer. METHODS: This multicenter, retrospective study included 1516 patients with resected pancreatic ductal adenocarcinoma (PDAC) from 5 centers located in China. The discovery cohort (n=763), which included preoperative multiphase CECT scans and OS data from 2 centers, was used to construct a fully automated imaging-derived prognostic biomarker-DeepCT-PDAC-by training scalable deep segmentation and prognostic models (via self-learning) to comprehensively model the tumor-anatomy spatial relations and their appearance dynamics in multiphase CECT for OS prediction. The marker was independently tested using internal (n=574) and external validation cohorts (n=179, 3 centers) to evaluate its performance, robustness, and clinical usefulness. RESULTS: Preoperatively, DeepCT-PDAC was the strongest predictor of OS in both internal and external validation cohorts [hazard ratio (HR) for high versus low risk 2.03, 95% confidence interval (CI): 1.50-2.75; HR: 2.47, CI: 1.35-4.53] in a multivariable analysis. Postoperatively, DeepCT-PDAC remained significant in both cohorts (HR: 2.49, CI: 1.89-3.28; HR: 2.15, CI: 1.14-4.05) after adjustment for potential confounders. For margin-negative patients, adjuvant chemoradiotherapy was associated with improved OS in the subgroup with DeepCT-PDAC low risk (HR: 0.35, CI: 0.19-0.64), but did not affect OS in the subgroup with high risk. CONCLUSIONS: Deep learning-based CT imaging-derived biomarker enabled the objective and unbiased OS prediction for patients with resectable PDAC. This marker is applicable across hospitals, imaging protocols, and treatments, and has the potential to tailor neoadjuvant and adjuvant treatments at the individual level.

Artificial Intelligence to Predict Lymph Node Metastasis at CT in Pancreatic Ductal Adenocarcinoma.

Background Although deep learning has brought revolutionary changes in health care, reliance on manually selected cross-sectional images and segmentation remain methodological barriers. Purpose To develop and validate an automated preoperative artificial intelligence (AI) algorithm for tumor and lymph node (LN) segmentation with CT imaging for prediction of LN metastasis in patients with pancreatic ductal adenocarcinoma (PDAC). Materials and Methods In this retrospective study, patients with surgically resected, pathologically confirmed PDAC underwent multidetector CT from January 2015 to April 2020. Three models were developed, including an AI model, a clinical model, and a radiomics model. CT-determined LN metastasis was diagnosed by radiologists. Multivariable logistic regression analysis was conducted to develop the clinical and radiomics models. The performance of the models was determined on the basis of their discrimination and clinical utility. Kaplan-Meier curves, the log-rank test, or Cox regression were used for survival analysis. Results Overall, 734 patients (mean age, 62 years ± 9 [SD]; 453 men) were evaluated. All patients were split into training (n = 545) and validation (n = 189) sets. Patients who had LN metastasis (LN-positive group) accounted for 340 of 734 (46%) patients. In the training set, the AI model showed the highest performance (area under the receiver operating characteristic curve [AUC], 0.91) in the prediction of LN metastasis, whereas the radiologists and the clinical and radiomics models had AUCs of 0.58, 0.76, and 0.71, respectively. In the validation set, the AI model showed the highest performance (AUC, 0.92) in the prediction of LN metastasis, whereas the radiologists and the clinical and radiomics models had AUCs of 0.65, 0.77, and 0.68, respectively (P < .001). AI model-predicted positive LN metastasis was associated with worse survival (hazard ratio, 1.46; 95% CI: 1.13, 1.89; P = .004). Conclusion An artificial intelligence model outperformed radiologists and clinical and radiomics models for prediction of lymph node metastasis at CT in patients with pancreatic ductal adenocarcinoma. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Chu and Fishman in this issue.

Behavior of iron and other heavy metals in passivated sediments and the coupling effect on phosphorus.

In situ passivation, which is easy to operate and affordable, is one of the most commonly used methods for sediment phosphorus (P) remediation. Understanding the behavior of iron and other heavy metals in passivated sediments is important for alleviating lake eutrophication and for ensuring drinking water safety. In this study, we investigated the behavior of P, Fe, Mn, Cd, Co, and Pb in lanthanum modified bentonite (LMB, Phoslock®) and polyaluminum chloride (PAC)-passivated sediments using intact sediment cores. Rhizon sampler and diffusive gradients in thin films technology (DGT) were respectively used to collect soluble and labile substances in sediment; a modified sequential selective extraction method was used to characterize metal forms. Results showed that LMB reduced soluble reactive phosphorus (SRP) at sediment depths of 0 ~ -15 mm and DGT-labile P flux at 0 ~ -50 mm. Correlation between DGT-labile P and Fe (R2 = 0.71) indicated that P mobility in the LMB group was affected by the behavior of Fe. PAC decreased SRP at sediment depths of 0, -5, -10, -15, -20, -25, and -50 mm with removal rates of 100%, 90%, 45%, 35%, 81%, 89%, and 100%, respectively. DGT-labile P flux was decreased by PAC at 0 ~ -10 mm and -50 ~ -110 mm, but increased at -10 ~ -50 mm; this is a result of synthetical effect by Al flocs adsorption and Fe(III) reductive dissolution. LMB decreased Cd, Co, and Pb in LMB layer in carbonate, reducible, and oxidizable forms. PAC decreased Cd mobility but caused the transformation of Co and Pb from reducible to other forms because of Fe(III) reductive dissolution. Those results indicate that sedimentary Fe plays an important role in in situ passivation. We suggest modifying passivators to Fe(II) adsorbents and increasing DO permeability of sediment to promote the formation of an Fe(III) passivation layer and hence the effectiveness of P control.

DeepPrognosis: Preoperative prediction of pancreatic cancer survival and surgical margin via comprehensive understanding of dynamic contrast-enhanced CT imaging and tumor-vascular contact parsing.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers and carries a dismal prognosis of ∼10% in five year survival rate. Surgery remains the best option of a potential cure for patients who are evaluated to be eligible for initial resection of PDAC. However, outcomes vary significantly even among the resected patients who were the same cancer stage and received similar treatments. Accurate quantitative preoperative prediction of primary resectable PDACs for personalized cancer treatment is thus highly desired. Nevertheless, there are a very few automated methods yet to fully exploit the contrast-enhanced computed tomography (CE-CT) imaging for PDAC prognosis assessment. CE-CT plays a critical role in PDAC staging and resectability evaluation. In this work, we propose a novel deep neural network model for the survival prediction of primary resectable PDAC patients, named as 3D Contrast-Enhanced Convolutional Long Short-Term Memory network (CE-ConvLSTM), which can derive the tumor attenuation signatures or patterns from patient CE-CT imaging studies. Tumor-vascular relationships, which might indicate the resection margin status, have also been proven to hold strong relationships with the overall survival of PDAC patients. To capture such relationships, we propose a self-learning approach for automated pancreas and peripancreatic anatomy segmentation without requiring any annotations on our PDAC datasets. We then employ a multi-task convolutional neural network (CNN) to accomplish both tasks of survival outcome and margin prediction where the network benefits from learning the resection margin related image features to improve the survival prediction. Our presented framework can improve overall survival prediction performances compared with existing state-of-the-art survival analysis approaches. The new staging biomarker integrating both the proposed risk signature and margin prediction has evidently added values to be combined with the current clinical staging system.

Deep Learning for Fully Automated Prediction of Overall Survival in Patients with Oropharyngeal Cancer Using FDG-PET Imaging.

PURPOSE: Accurate prognostic stratification of patients with oropharyngeal squamous cell carcinoma (OPSCC) is crucial. We developed an objective and robust deep learning-based fully-automated tool called the DeepPET-OPSCC biomarker for predicting overall survival (OS) in OPSCC using [18F]fluorodeoxyglucose (FDG)-PET imaging. EXPERIMENTAL DESIGN: The DeepPET-OPSCC prediction model was built and tested internally on a discovery cohort (n = 268) by integrating five convolutional neural network models for volumetric segmentation and ten models for OS prognostication. Two external test cohorts were enrolled-the first based on the Cancer Imaging Archive (TCIA) database (n = 353) and the second being a clinical deployment cohort (n = 31)-to assess the DeepPET-OPSCC performance and goodness of fit. RESULTS: After adjustment for potential confounders, DeepPET-OPSCC was found to be an independent predictor of OS in both discovery and TCIA test cohorts [HR = 2.07; 95% confidence interval (CI), 1.31-3.28 and HR = 2.39; 95% CI, 1.38-4.16; both P = 0.002]. The tool also revealed good predictive performance, with a c-index of 0.707 (95% CI, 0.658-0.757) in the discovery cohort, 0.689 (95% CI, 0.621-0.757) in the TCIA test cohort, and 0.787 (95% CI, 0.675-0.899) in the clinical deployment test cohort; the average time taken was 2 minutes for calculation per exam. The integrated nomogram of DeepPET-OPSCC and clinical risk factors significantly outperformed the clinical model [AUC at 5 years: 0.801 (95% CI, 0.727-0.874) vs. 0.749 (95% CI, 0.649-0.842); P = 0.031] in the TCIA test cohort. CONCLUSIONS: DeepPET-OPSCC achieved an accurate OS prediction in patients with OPSCC and enabled an objective, unbiased, and rapid assessment for OPSCC prognostication.

Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks.

Traditional image-based survival prediction models rely on discriminative patch labeling which make those methods not scalable to extend to large datasets. Recent studies have shown Multiple Instance Learning (MIL) framework is useful for histopathological images when no annotations are available in classification task. Different to the current image-based survival models that limit to key patches or clusters derived from Whole Slide Images (WSIs), we propose Deep Attention Multiple Instance Survival Learning (DeepAttnMISL) by introducing both siamese MI-FCN and attention-based MIL pooling to efficiently learn imaging features from the WSI and then aggregate WSI-level information to patient-level. Attention-based aggregation is more flexible and adaptive than aggregation techniques in recent survival models. We evaluated our methods on two large cancer whole slide images datasets and our results suggest that the proposed approach is more effective and suitable for large datasets and has better interpretability in locating important patterns and features that contribute to accurate cancer survival predictions. The proposed framework can also be used to assess individual patient's risk and thus assisting in delivering personalized medicine.

Influences of fine-grained NaCl particles on performance and denitration efficiency of activated carbon during sintering flue gas purification process.

Alkali metal chlorides emitted from sintering flue gas are easily adsorbed on the surface of activated carbon (AC) in the purification process. In this paper, NaCl particles adsorbing onto AC were simulated by impregnation method, and the size and morphology of NaCl particles were similar to those of NaCl-PM10 emitted from sintering flue gas. With the adsorption of NaCl particles, 2-10-µm pores of AC were filled, and the specific surface area of AC decreased. But NaCl led to the increase of acidic functional groups on the surface of AC. When 0.75 wt% NaCl was adsorbed, it was beneficial for AC catalytic denitration (de-NOx), because the chemical reaction was strengthened by acidic functional groups, so it showed a certain promotion of de-NOx efficiency. As 1.5 wt% NaCl and 3 wt% NaCl were adsorbed, NaCl had an inhibitory effect on AC de-NOx, which was because the specific surface area of AC decreased, and the prevention of physical adsorption played a major role. As a result, the de-NOx efficiency of AC adsorbed with 3 wt% NaCl decreased from 40.59 to 23.02% at 150 °C. Therefore, the absorption of NaCl fine particles on AC should not exceed 0.75 wt%.

China-Africa cooperation initiatives in malaria control and elimination.

Malaria has affected human health globally with a significant burden of disease, and also has impeded social and economic development in the areas where it is present. In Africa, many countries have faced serious challenges in controlling malaria, in part due to major limitations in public health systems and primary health care infrastructure. Although China is a developing country, a set of control strategies and measures in different local settings have been implemented successfully by the National Malaria Control Programme over the last 60 years, with a low cost of investment. It is expected that Chinese experience may benefit malaria control in Africa. This review will address the importance and possibility of China-Africa collaboration in control of malaria in targeted African countries, as well as how to proceed toward the goal of elimination where this is technically feasible.

Priorities for research and control of cestode zoonoses in Asia.

Globally, cestode zoonoses cause serious public health problems, particularly in Asia. Among all neglected zoonotic diseases, cestode zoonoses account for over 75% of global disability adjusted life years (DALYs) lost. An international symposium on cestode zoonoses research and control was held in Shanghai, China between 28th and 30th October 2012 in order to establish joint efforts to study and research effective approaches to control these zoonoses. It brought together 96 scientists from the Asian region and beyond to exchange ideas, report on progress, make a gap analysis, and distill prioritizing settings with a focus on the Asian region. Key objectives of this international symposium were to agree on solutions to accelerate progress towards decreasing transmission, and human mortality and morbidity caused by the three major cestode zoonoses (cystic echinococcosis, alveolar echinococcosis, and cysticercosis); to critically assess the potential to control these diseases; to establish a research and validation agenda on existing and new approaches; and to report on novel tools for the study and control of cestode zoonoses.