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Time-dependent ROC analyses, 2-year survival, DeepPET-OPSCC score, clinical factors, nomograms, clinical deployment test cohort

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.

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Background Alectinib is approved for the treatment of advanced non-small-cell lung cancer (NSCLC) harboring ALK rearrangements. Although generally well tolerated, alectinib can cause serious or life-threatening side effects. Case Presentation Here, we report a case of a patient with NSCLC with an EML4-ALK fusion and was treated with alectinib but who developed grade 4 hyperbilirubinemia after five months on therapy. Alectinib was discontinued, and an artificial liver support system (ALSS) was used with an impressive decline in bilirubin levels. After two months drug-free, the patient experienced disease progression. Ensartinib was initiated as second-line treatment with a best response of stable disease after three months of therapy with no evidence of hyperbilirubinemia. Conclusion This is the first report of ensartinib treatment after alectinib-induced hyperbilirubinemia which was successfully relieved by ALSS treatment and targeted drug cessation.

Objective To develop a fully automated AI system to quantitatively assess the disease severity and disease progression of COVID-19 using thick-section chest CT images. Methods In this retrospective study, an AI system was developed to automatically segment and quantify the COVID-19-infected lung regions on thick-section chest CT images. Five hundred thirty-one CT scans from 204 COVID-19 patients were collected from one appointed COVID-19 hospital. The automatically segmented lung abnormalities were compared with manual segmentation of two experienced radiologists using the Dice coefficient on a randomly selected subset (30 CT scans). Two imaging biomarkers were automatically computed, i.e., the portion of infection (POI) and the average infection HU (iHU), to assess disease severity and disease progression. The assessments were compared with patient status of diagnosis reports and key phrases extracted from radiology reports using the area under the receiver operating characteristic curve (AUC) and Cohen’s kappa, respectively. Results The dice coefficient between the segmentation of the AI system and two experienced radiologists for the COVID-19-infected lung abnormalities was 0.74 ± 0.28 and 0.76 ± 0.29, respectively, which were close to the inter-observer agreement (0.79 ± 0.25). The computed two imaging biomarkers can distinguish between the severe and non-severe stages with an AUC of 0.97 (p value < 0.001). Very good agreement (κ = 0.8220) between the AI system and the radiologists was achieved on evaluating the changes in infection volumes. Conclusions A deep learning–based AI system built on the thick-section CT imaging can accurately quantify the COVID-19-associated lung abnormalities and assess the disease severity and its progressions. Key Points • A deep learning–based AI system was able to accurately segment the infected lung regions by COVID-19 using the thick-section CT scans (Dice coefficient ≥ 0.74). • The computed imaging biomarkers were able to distinguish between the non-severe and severe COVID-19 stages (area under the receiver operating characteristic curve 0.97). • The infection volume changes computed by the AI system were able to assess the COVID-19 progression (Cohen’s kappa 0.8220). Electronic supplementary material The online version of this article (10.1007/s00330-020-07042-x) contains supplementary material, which is available to authorized users.

The causes of irritable bowel syndrome remain unknown. Studies and meta-analyses revealed that intestinal microbiota disturbance was one of the causes of irritable bowel syndrome, but the results remained controversial. Therefore, we performed a systematic review and meta-analysis to identify the association between them. We performed a systematic meta-analysis of case-control studies from January 2000 to December 2020 to compare fecal microbes based on polymerase chain reaction and bacterial cul- ture between adult irritable bowel syndrome patients and healthy controls. The standardized mean difference value and a 95% CI were calculated. Two professional researchers used Newcastle-Ottawa Scale to reassess selected literature and extract high- quality studies. Six studies were included in our analysis. When all eligible studies were pooled into the meta-analysis, compared with healthy controls, the standardized mean differences of Bifidobacteria (standardized mean difference = -1.01, 95% CI =: -2.01 to -0.01) in irritable bowel syndrome patients decreased significantly, whereas the standardized mean differences of Enterococcus, Enterobacter, Lactobacillus, Bacteroides, and Escherichia coli did not change significantly in irritable bowel syndrome patients. However, heterogeneity was significant to perform sensitivity analysis and stratified analysis in all these special intestinal microbes. In summary, this study indicated that only Bifidobacteria was decreased in irritable bowel syndrome patients compared with healthy controls using Newcastle-Ottawa Scale standards to extract high-quality literature. Future studies are warranted to further dem- onstrate the relationship between them.

Simple Summary Glioblastoma multiforme (GBM) and solitary brain metastasis (SBM) are common brain tumors in adults. The two tumors often pose a diagnostic dilemma owing to their similar features on conventional magnetic resonance imaging (MRI). Ability to discriminate the two tumors is critical as it informs clinical treatment strategies. This pilot study attempts to employ the machine learning technique to identify GBM and SBM by fusing radiomics features of multiple MRI sequences and multiple models. A multiparametric MR-based RadioFusionOmics (RFO) model was developed and has demonstrated promising prediction accuracy for the identifications of GBM and SBM. Abstract This study aimed to evaluate the diagnostic potential of a novel RFO model in differentiating GBM and SBM with multiparametric MR sequences collected from 244 (131 GBM and 113 SBM) patients. Three basic volume of interests (VOIs) were delineated on the conventional axial MR images (T1WI, T2WI, T2_FLAIR, and CE_T1WI), including volumetric non-enhanced tumor (nET), enhanced tumor (ET), and peritumoral edema (pTE). Using the RFO model, radiomics features extracted from different multiparametric MRI sequence(s) and VOI(s) were fused and the best sequence and VOI, or possible combinations, were determined. A multi-disciplinary team (MDT)-like fusion was performed to integrate predictions from the high-performing models for the final discrimination of GBM vs. SBM. Image features extracted from the volumetric ET (VOIET) had dominant predictive performances over features from other VOI combinations. Fusion of VOIET features from the T1WI and T2_FLAIR sequences via the RFO model achieved a discrimination accuracy of AUC = 0.925, accuracy = 0.855, sensitivity = 0.856, and specificity = 0.853, on the independent testing cohort 1, and AUC = 0.859, accuracy = 0.836, sensitivity = 0.708, and specificity = 0.919 on the independent testing cohort 2, which significantly outperformed three experienced radiologists (p = 0.03, 0.01, 0.02, and 0.01, and p = 0.02, 0.01, 0.45, and 0.02, respectively) and the MDT-decision result of three experienced experts (p = 0.03, 0.02, 0.03, and 0.02, and p = 0.03, 0.02, 0.44, and 0.03, respectively).

Accurate organ-at-risk (OAR) segmentation is critical to reduce radiotherapy complications. Consensus guidelines recommend delineating over 40 OARs in the head-and-neck (H&N). However, prohibitive labor costs cause most institutions to delineate a substantially smaller subset of OARs, neglecting the dose distributions of other OARs. Here, we present an automated and highly effective stratified OAR segmentation (SOARS) system using deep learning that precisely delineates a comprehensive set of 42 H&N OARs. We train SOARS using 176 patients from an internal institution and independently evaluate it on 1327 external patients across six different institutions. It consistently outperforms other state-of-the-art methods by at least 3–5% in Dice score for each institutional evaluation (up to 36% relative distance error reduction). Crucially, multi-user studies demonstrate that 98% of SOARS predictions need only minor or no revisions to achieve clinical acceptance (reducing workloads by 90%). Moreover, segmentation and dosimetric accuracy are within or smaller than the inter-user variation.

Accurate organ at risk (OAR) segmentation is critical to reduce the radiotherapy post-treatment complications. Consensus guidelines recommend a set of more than 40 OARs in the head and neck (H&N) region, however, due to the predictable prohibitive labor-cost of this task, most institutions choose a substantially simplified protocol by delineating a smaller subset of OARs and neglecting the dose distributions associated with other OARs. In this work we propose a novel, automated and highly effective stratified OAR segmentation (SOARS) system using deep learning to precisely delineate a comprehensive set of 42 H&N OARs. SOARS stratifies 42 OARs into anchor, mid-level, and small & hard subcategories, with specifically derived neural network architectures for each category by neural architecture search (NAS) principles. We built SOARS models using 176 training patients in an internal institution and independently evaluated on 1327 external patients across six different institutions. It consistently outperformed other state-of-the-art methods by at least 3-5% in Dice score for each institutional evaluation (up to 36% relative error reduction in other metrics). More importantly, extensive multi-user studies evidently demonstrated that 98% of the SOARS predictions need only very minor or no revisions for direct clinical acceptance (saving 90% radiation oncologists workload), and their segmentation and dosimetric accuracy are within or smaller than the inter-user variation. These findings confirmed the strong clinical applicability of SOARS for the OAR delineation process in H&N cancer radiotherapy workflows, with improved efficiency, comprehensiveness, and quality.

Hydrocephalus induced by intraventricular hemorrhage (IVH) is associated with unfavorable prognosis. The increased permeability of choroid plexus and breakdown of the blood–brain barrier (BBB) was reported as a prominent mechanism of IVH-induced hydrocephalus, and vascular endothelial–cadherin (VE–cadherin) was demonstrated to be relevant. Metformin was reported to protect endothelial junction and preserve permeability widely; however, its role in hydrocephalus remains unclear. In this study, the decreased expression of VE–cadherin in the choroid plexus, accompanied with ventricle dilation, was investigated in an IVH rat model induced by intraventricular injection of autologous blood. Metformin treatment ameliorated hydrocephalus and upregulated VE–cadherin expression in choroid plexus meanwhile. We then observed that the internalization of VE–cadherin caused by the activation of vascular endothelial growth factor (VEGF) signaling after IVH was related to the occurrence of hydrocephalus, whereas it can be reversed by metformin treatment. Restraining VEGF signaling by antagonizing VEGFR2 or inhibiting Src phosphorylation increased the expression of VE–cadherin and decreased the severity of hydrocephalus after IVH. Our study demonstrated that the internalization of VE–cadherin via the activation of VEGF signaling may contribute to IVH-induced hydrocephalus, and metformin may be a potential protector via suppressing this pathway.

Purpose/Objective(s) Identifying suspicious cancer metastasized lymph nodes (GTV is an essential task in esophageal cancer radiotherapy. Manual delineation requires high-level sophisticated clinical reasoning in multi-modality imaging, which is challenging and suffers from high subjectivity and inter-observer variability. We propose an automated esophageal GTV_LN detection method via integrating a distance-based decision stratification into a multi-task deep network, which is able to effectively learn the GTV_LN oncology features. This is inspired by the fact that lymph node involvements often follow certain distance patterns from the primary tumors. Materials/Methods We collected and curated 2 esophageal cancer datasets with PET and RTCT images. The 1st dataset contained 141 patients and 651 annotated GTV_LN for training and evaluating our deep network (60%, 10% and 30% for training, validation and testing), while the 2nd dataset contained 10 patients aiming for the multi-user agreement analysis. A distance-based decision stratification is designed to divide GTV_LN into “tumor-proximal” and “tumor-distal” categories and a multi-branch deep network is proposed to solve each of them. The multi-branch network has a shared encoder and separate decoders to detect and segment two GTV_LN subcategories, respectively. The distance-based decision stratification enables the automated multi-branch method to specialize in GTV_LN oncology features of each subcategory. We use both PET and RTCT as the network inputs to exploit the complementary information in each imaging modality. Results In the testing set of the 1st esophageal cancer dataset (34 patients with 138 GTV_LNs), our method achieves a sensitivity of 73.8% and 79.1% at 6 and 12 false positives (FPs) per patient as compared to 72.0% and 75.9% by MULAN, the state-of-the-art deep learning based universal lesion detection algorithm. On the 2nd dataset containing 10 patients, 3 radiation oncologists annotated 53 GTV_LNs in total, among which 42 GTV_LNs were agreed with all 3 physicians leading to 79.2% multi-user agreement. Our method achieves a sensitivity of 77.4% at 6 FPs per patient on the 2nd dataset. This shows that the sensitivity achieved by our automated deep learning approach can be close to the multi-user agreement. Conclusion We proposed a multi-task deep learning method with distance-based decision stratification to effectively detect and segment the GTV_LN in esophageal cancer radiotherapy. It significantly improves on previous state-of-the-art and the achieved performance has potential clinical values considering the large multi-user variability in this challenging task.

Lymph node station (LNS) delineation from computed tomography (CT) scans is an indispensable step in radiation oncology workflow. High inter-user variabilities across oncologists and prohibitive laboring costs motivated the automated approach. Previous works exploit anatomical priors to infer LNS based on predefined ad-hoc margins. However, without the voxel-level supervision, the performance is severely limited. LNS is highly context-dependent—LNS boundaries are constrained by anatomical organs—we formulate it as a deep spatial and contextual parsing problem via encoded anatomical organs. This permits the deep network to better learn from both CT appearance and organ context. We develop a stratified referencing organ segmentation protocol that divides the organs into anchor and non-anchor categories and uses the former’s predictions to guide the later segmentation. We further develop an auto-search module to identify the key organs that opt for the optimal LNS parsing performance. Extensive four-fold cross-validation experiments on a dataset of 98 esophageal cancer patients (with the most comprehensive set of 12 LNSs + 22 organs in thoracic region to date) are conducted. Our LNS parsing model produces significant performance improvements, with an average Dice score of \(81.1\%\pm 6.1\%\), which is 5.0% and 19.2% higher over the pure CT-based deep model and the previous representative approach, respectively.

In this work, we introduce a fast and accurate method for unsupervised 3D medical image registration. This work is built on top of a recent algorithm self-supervised anatomical embedding (SAM), which is capable of computing dense anatomical/semantic correspondences between two images at the pixel level. Our method is named SAM-enhanced registration (SAME), which breaks down image registration into three steps: affine transformation, coarse deformation, and deep deformable registration. Using SAM embeddings, we enhance these steps by finding more coherent correspondences, and providing features and a loss function with better semantic guidance. We collect a multi-phase chest computed tomography dataset with 35 annotated organs for each patient and conduct inter-subject registration for quantitative evaluation. Results show that SAME outperforms widely-used traditional registration techniques (Elastix FFD, ANTs SyN) and learning based VoxelMorph method by at least \(4.7\%\) and \(2.7\%\) in Dice scores for two separate tasks of within-contrast-phase and across-contrast-phase registration, respectively. SAME achieves the comparable performance to the best traditional registration method, DEEDS (from our evaluation), while being orders of magnitude faster (from 45 s to 1.2 s).

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.

Background The epidemiology of esophageal cancer (EC) can elucidate its causes and risk factors and help develop prevention strategies. We aimed to provide an overview of the burden, trends, and risk factors of EC in China from 1990 to 2017. We also investigated the differences between China, Japan, and South Korea and discussed the possible causes of the disparities. Methods We used the Global Burden of Disease Study 2017 to obtain data on incident cases, deaths, disability-adjusted life-year (DALY) cases, age-standardized incidence rate (ASIR), age-standardized death rate (ASDR), and age-standardized DALY rate of EC in China, Japan, and South Korea from 1990 to 2017. Trend analysis was performed using joinpoint analysis. We measured the associations between ASIR, ASDR, and age-standardized DALY rate and the socio-demographic index (SDI) for 1990–2017. We also analyzed the risk factors associated with EC deaths and DALYs. Results China recorded 234,624 (95% uncertainty intervals: 223,240–246,036) incident cases of and 212,586 (202,673–222,654) deaths from EC in 2017. The ASIR and ASDR declined from 1990 to 2017. Until 2017, the ASIR was 12.23, and ASDR was 11.25 per 100,000 persons. The DALYs were 4,464,980 (4,247,816–4,690,846) with an age-standardized rate of 222.58 per 100,000 persons in 2017. The ASIR, ASDR, and age-standardized DALY rate in China were twice those of Japan and South Korea. These three indicators showed a decreasing trend, whereas SDI increased, in all three countries from 1990 to 2017. Tobacco and alcohol use remained the major risk factors for EC death and DALYs, especially for men in China and women in Japan and South Korea. High body mass index (BMI) and low-fruit diet were the main risk factors for women in China. Conclusions The incident cases and deaths of EC in China, Japan, and South Korea increased from 1990 to 2017, whereas the ASIR, ASDR, and age-standardized DALY rate declined. China had the greatest burden of EC among three countries. SDI and aging along with tobacco use, alcohol use, high BMI, and low-fruit diet were the main risk factors of death and DALYs and should be paid more attention.

BackgroundThick-section CT scanners are more affordable for the developing countries. Considering the widely spread COVID-19, it is of great benefit to develop an automated and accurate system for quantification of COVID-19 associated lung abnormalities using thick-section chest CT images.PurposeTo develop a fully automated AI system to quantitatively assess the disease severity and disease progression using thick-section chest CT images.Materials and MethodsIn this retrospective study, a deep learning based system was developed to automatically segment and quantify the COVID-19 infected lung regions on thick-section chest CT images. 531 thick-section CT scans from 204 patients diagnosed with COVID-19 were collected from one appointed COVID-19 hospital from 23 January 2020 to 12 February 2020. The lung abnormalities were first segmented by a deep learning model. To assess the disease severity (non-severe or severe) and the progression, two imaging bio-markers were automatically computed, i.e., the portion of infection (POI) and the average infection HU (iHU). The performance of lung abnormality segmentation was examined using Dice coefficient, while the assessment of disease severity and the disease progression were evaluated using the area under the receiver operating characteristic curve (AUC) and the Cohen’s kappa statistic, respectively.ResultsDice coefficient between the segmentation of the AI system and the manual delineations of two experienced radiologists for the COVID-19 infected lung abnormalities were 0.74±0.28 and 0.76±0.29, respectively, which were close to the inter-observer agreement, i.e., 0.79±0.25. The computed two imaging bio-markers can distinguish between the severe and non-severe stages with an AUC of 0.9680 (p-value< 0.001). Very good agreement (κ = 0.8220) between the AI system and the radiologists were achieved on evaluating the changes of infection volumes.ConclusionsA deep learning based AI system built on the thick-section CT imaging can accurately quantify the COVID-19 associated lung abnormalities, assess the disease severity and its progressions.Key ResultsA deep learning based AI system was able to accurately segment the infected lung regions by COVID-19 using the thick-section CT scans (Dice coefficient ≥ 0.74).The computed imaging bio-markers were able to distinguish between the non-severe and severe COVID-19 stages (area under the receiver operating characteristic curve 0.968).The infection volume changes computed by the AI system was able to assess the COVID-19 progression (Cohen’s kappa 0.8220).Summary StatementA deep learning based AI system built on the thick-section CT imaging can accurately quantify the COVID-19 infected lung regions, assess patients disease severity and their disease progressions.

// Ling Peng 1 , Xianghua Ye 2 , Yun Hong 3 , Junyan Zhang 4 , Yongquan Dong 5 and Qiong Zhao 1 1 Department of Thoracic Oncology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China 2 Department of Radiotherapy, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China 3 Department of Pharmacy, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China 4 Bothwin Clinical Study Consultant, Bellevue, WA, USA 5 Department of Respiratory Disease, Yinzhou No.2 Hospital, Ningbo, Zhejiang Province, China Correspondence to: Qiong Zhao, email: zhaoqiong@zju.edu.cn Keywords: apatinib; hypertension; proteinuria; hand-foot-syndrome; meta-analysis Received: July 19, 2017 Accepted: November 03, 2017 Epub: January 13, 2018 Published: August 14, 2018 ABSTRACT Background: Apatinib is a novel small molecular drug targeting vascular endothelial growth factor receptor-2 (VEGFR-2), which is being studied in multiple tumor types. We performed a meta-analysis to quantify the overall incidence and risk of hypertension, proteinuria, and hand-foot-syndrome (HFS) in cancer patients receiving apatinib. Results: Altogether, 820 cancer patients from 7 prospective trials were included for the meta-analysis. The incidences of all-grade and high-grade hypertension were 45.4% and 9.7%. The incidences of all-grade and high-grade proteinuria were 45.1% and 3.7%. The incidences of all-grade and high-grade HFS were 35.9% and 8.6%. The RRs of all-grade hypertension, proteinuria and HFS of apatinib compared to placebo were increased (hypertension, RR = 6.53; proteinuria, RR = 2.62, and HFS, RR = 11.45). The RRs of developing high-grade hypertension and HFS were substantially higher than that of placebo (hypertension, RR = 7.73; HFS, RR = 7.23), but not for proteinuria (RR = 2.56, 95% CI: 0.57–11.52). Materials and Methods: Prospective phase II and III clinical trials of cancer patients receiving apatinib were searched and included. Summary incidences, relative risk (RR), and 95% confidence intervals (CI) were calculated by using either fixed or random effects models according to the heterogeneity of the studies. Conclusions: Apatinib is generally well tolerated, and associated with increased risks of all-grade hypertension, proteinuria and HFS, and high-grade hypertension and HFS, but not high-grade proteinuria.

// Ling Peng 1 , Yun Hong 2 , Xianghua Ye 3 , Peng Shi 4, 5 , Junyan Zhang 6 , Yina Wang 1 and Qiong Zhao 1 1 Department of Thoracic Oncology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China 2 Department of Pharmacy, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China 3 Department of Radiotherapy, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China 4 Department of Medical Statistics, Children’s Hospital of Fudan University, Shanghai, China 5 Center for Evidence-Based Medicine, Fudan University, Shanghai, China 6 Bothwin Clinical Study Consultant, Seattle, WA, USA Correspondence to: Qiong Zhao, email: doczq.2008@hotmail.com Keywords: eribulin, peripheral neuropathy, incidence, relative risk, meta-analysis Received: July 10, 2017 Accepted: August 30, 2017 Published: September 19, 2017 ABSTRACT Background: Eribulin is a microtubule inhibitor, which is approved for the treatment of breast cancer. Peripheral neuropathy has been reported in the studies of eribulin, but the incidence and relative risk (RR) of eribulin-associated peripheral neuropathy varied greatly in cancer patients. The purpose of this meta-analysis was to determine the overall incidence and RR of eribulin-associated peripheral neuropathy in cancer patients. Materials and Methods: Pubmed database and Embase and abstracts presented at the American Society of Clinical Oncology (ASCO) meetings were systematically reviewed for primary studies. Eligible studies included prospective clinical trials and expanded access programs of cancer patients treated with eribulin. Statistical analyses were performed to calculate the incidences, RRs, and 95% confidence intervals (CIs). Results: Altogether, 4,849 patients from 19 clinical trials were selected for this meta-analysis. The incidences of all-grade and high-grade peripheral neuropathy were 27.5% (95% CI: 23.3–32.4%) and 4.7% (95% CI: 3.6–6.2%), respectively. The relative risks of peripheral neuropathy of eribulin compared to control were increased for all-grade (RR = 1.89, 95% CI: 1.10–3.25) but not statistically significant for high-grade (RR = 2.98, 95% CI: 0.71–12.42). Conclusions: The use of eribulin is associated with an increased incidence of peripheral neuropathy. The RR is increased for all-grade peripheral neuropathy.

Ling Peng,1 Yina Wang,1 Yemin Tang,1 Lei Zeng,1 Junfang Liu,1 Zhu Zeng,1 Jian Liu,1 Peng Shi,2,3 Xianghua Ye,4 Qiong Zhao1 1Department of Thoracic Oncology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 2Department of Medical Statistics, Children’s Hospital of Fudan University, 3Center for Evidence Based Medicine, Fudan University, Shanghai, 4Department of Radiotherapy, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China Background: Several clinical studies have demonstrated that continuous administration of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) could provide additional survival benefit for advanced non-small cell lung cancer (NSCLC) patients who had benefited from prior EGFR TKI therapy. However, whether EGFR TKI combined with chemotherapy could further prolong survival in patients with gradual progression is still unclear. The present study was conducted to evaluate the clinical outcome of continuous EGFR TKI treatment in combination with chemotherapy (combination group) versus continuous EGFR TKI treatment only (monotherapy group) in such a clinical setting.Methods: We designed a cohort study to collect all chart data of NSCLC patients treated with EGFR TKI in our institution from February 2012 to December 2015 retrospectively and followed up the clinical outcome of EGFR TKI monotherapy or therapy in combination with chemotherapy until April 2017 prospectively. All eligible patients had to meet the criteria of gradual progression. The time interval of progression-free survival 1 (PFS1, gradual progression or death) to PFS2 (off-EGFR TKI progression), and overall survival (OS) between the above 2groups were used in survival analysis.Results: In all, 50 patients were included in our study. Patients’ baseline characteristics were well balanced. Exon 19 deletion mutations and L858R point mutations were detected in 16 and 8patients, respectively. Twenty, 22, and 8 patients were treated with EGFR TKI in the first, second, and third line setting, respectively. The time interval from PFS1 to PFS2 was 92 and 37days (monotherapy vs combination), respectively (hazard ratio [HR] =1.16, 95% confidence interval [CI]: 0.61–2.21, P=0.652). The median OS in the monotherapy group and combination group was 696 and 799 days, respectively (HR =0.74, 95% CI: 0.33–1.71, P=0.501). There were no statistical differences between the 2 groups in terms of the time interval from PFS1 to PFS2 and OS.Conclusion: Our results suggested that compared with EGFR TKI monotherapy, its combination with chemotherapy beyond gradual progression may not confer a significant survival benefit to NSCLC patients. Further prospective studies are warranted to reinforce the results of the study. Keywords: epidermal growth factor receptor, tyrosine kinase inhibitor, non-small cell lung cancer

Background Investigations of disease incidence, mortality, and disability-adjusted life years (DALYs) are valuable for facilitating preventive measures and health resource planning. We examined the tracheal, bronchus, and lung (TBL) cancer burdens worldwide according to sex, age, and social development index (SDI) at the global, regional, and national levels. Methods We assessed the TBL cancer burden using data from the Global Burden of Disease (GBD) database, including 21 regions, 195 countries, and territories in the diagnostic period 1990–2017. The data of TBL cancer-related mortality and DALYs attributable to all known risk factors were also analyzed. Age-standardized rates (ASRs) and their estimated annual percentage changes (EAPCs) were calculated. Results Incident cases, deaths, and DALYs of TBL cancer increased worldwide (100.44%, 82.30%, and 61.27%, respectively). The age-standardized incidence rate (ASIR) was stable (EAPC = 0.02, 95% confidence interval [CI] − 0.03 to 0.08), but the age-standardized death (EAPC = − 0.34, 95%CI − 0.38 to − 0.3) and DALY rate decreased generally (EAPC = − 0.74, 95%CI − 0.8 to − 0.68). However, the change trend of ASIR and ASDR among sexes was on the contrary. China and the USA always had the highest incidence, mortality, and DALYs of TBL cancer. Significant positive correlations between ASRs and SDI were observed, especially among females. High (36.86%), high-middle (28.78%), and middle SDI quintiles (24.91%) carried the majority burden of TBL cancer. Tobacco remained the top cause of TBL cancer death and DALYs, followed by air pollution, the leading cause in the low-middle and low-SDI quintiles. Metabolic risk-related TBL cancer mortality and DALYs among females increased but was stable among males. The main ages of TBL cancer onset and death were > 50 years, and the DALYs concentrated in 50 − 69 years. Conclusions To significantly reduce the growing burden of TBL cancer, treatment resources need to be skewed according to factors such as risks and geography, especially for high-risk groups and high-burden areas. Asia had the greatest TBL cancer burden, followed by high-income North America. Tobacco remains the leading cause of death and DALYs, followed by air pollution. Effective prevention measures against tobacco and air pollution should be strengthened.

Figure S1. Age-specific rates for incidence, death, and DALY increased with age. DALY: disability-adjusted life-year.

Additional file 6: Table S3. The ASRs and variations of TBL cancer from 1990 to 2017 among countries.

Figure S3. Trends in percentage of esophageal cancer all-age death and ASDR due to risk factors in the world, East Asia and Pacific, China, Japan, and South Korea, from 1990 to 2017. (A) all-age death; (B) ASDR. ASDR: age-standardized death rate.

Additional file 4: Table S1. Three countries/regions with top and bottom burden of tracheal, bronchus, and lung cancer.

Figure S2. Trends for age-standardized DALY rate of esophageal cancer in China (A), Japan (B), South Korea (C), East Asia and Pacific (D), and the world (E) from 1990 to 2017 calculated by joinpoint regression analyses. DALY: disability-adjusted life-year.

Additional file 5: Table S2. The incidence, deaths, DALYs and variations of TBL cancer from 1990 to 2017 among countries.

Determining the spread of lymph node gross tumor volume (GTV\(_{LN}\)) is essential in defining the respective resection or irradiating regions for the downstream workflows of surgical resection and radiotherapy for many cancers. Different from the more common enlarged lymph node (LN), GTV\(_{LN}\) also includes smaller ones if associated with high positron emission tomography signals and/or any metastasis signs in CT. This is a daunting task. In this work, we propose a unified LN appearance and inter-LN relationship learning framework to detect the true GTV\(_{LN}\). This is motivated by the prior clinical knowledge that LNs form a connected lymphatic system, and the spread of cancer cells among LNs often follows certain pathways. Specifically, we first utilize a 3D convolutional neural network with ROI-pooling to extract the GTV\(_{LN}\)’s instance-wise appearance features. Next, we introduce a graph neural network to further model the inter-LN relationships where the global LN-tumor spatial priors are included in the learning process. This leads to an end-to-end trainable network to detect by classifying GTV\(_{LN}\). We operate our model on a set of GTV\(_{LN}\) candidates generated by a preliminary 1st-stage method, which has a sensitivity of \({>}85\%\) at the cost of high false positive (FP) (\({>}15\) FPs per patient). We validate our approach on a radiotherapy dataset with 142 paired PET/RTCT scans containing the chest and upper abdominal body parts. The proposed method significantly improves over the state-of-the-art (SOTA) LN classification method by \(5.5\%\) and \(13.1\%\) in F1 score and the averaged sensitivity value at 2, 3, 4, 6 FPs per patient, respectively.

Figure S4. Trends in percentage of esophageal cancer all-age and age-standardized DALY rate due to risk factors in the world, East Asia and Pacific, China, Japan, and South Korea, from 1990 to 2017. (A) all-age DALYs; (B) age-standardized DALY rate. DALY: disability-adjusted life-year.

Additional file 1. GBD overview.

Finding, identifying and segmenting suspicious cancer metastasized lymph nodes from 3D multi-modality imaging is a clinical task of paramount importance. In radiotherapy, they are referred to as Lymph Node Gross Tumor Volume (GTV\(_{LN}\)). Determining and delineating the spread of GTV\(_{LN}\) is essential in defining the corresponding resection and irradiating regions for the downstream workflows of surgical resection and radiotherapy of various cancers. In this work, we propose an effective distance-based gating approach to simulate and simplify the high-level reasoning protocols conducted by radiation oncologists, in a divide-and-conquer manner. GTV\(_{LN}\) is divided into two subgroups of “tumor-proximal" and “tumor-distal", respectively, by means of binary or soft distance gating. This is motivated by the observation that each category can have distinct though overlapping distributions of appearance, size and other LN characteristics. A novel multi-branch detection-by-segmentation network is trained with each branch specializing on learning one GTV\(_{LN}\) category features, and outputs from multi-branch are fused in inference. The proposed method is evaluated on an in-house dataset of 141 esophageal cancer patients with both PET and CT imaging modalities. Our results validate significant improvements on the mean recall from \(72.5\%\) to \(78.2\%\), as compared to previous state-of-the-art work. The highest achieved GTV\(_{LN}\) recall of \(82.5\%\) at \(20\%\) precision is clinically relevant and valuable since human observers tend to have low sensitivity (\(\sim \)80% for the most experienced radiation oncologists, as reported by literature [5]).

Additional file 5: Table S1. Incident cases, death cases, age-standardized incidence rate (ASIR), and age-standardized death rate (ASDR) for esophageal cancer in China, by geographic areas, 2015.

BackgroundOur previous studies demonstrated that intraventricular injection of thrombin could induce hydrocephalus. The inflammation of subarachnoid space plays a key role in hydrocephalus. As thrombin, inducing coagulation, could contribute to inflammation, its effects on subarachnoid space have not been well studied. Macrophagic dysfunction may contribute to this course. However, the mechanisms that how thrombin affects macrophage in subarachnoid space have not been illustrated. Our aim was to explore the possible role that macrophage played in thrombin-induced meningeal inflammation, and to furtherly understand its contribution during thrombin-induced hydrocephalus.MethodsThere were two parts in this study. Firstly, rats had an intraventricular injection of saline or thrombin. Secondly, rats received thrombin injection with vehicle or PAR1 antagonist treatment. Immunofluorescence staining was applied to observe the activation of meningeal macrophage and the expression of NeuN in the cortex. Meanwhile, the expression of intercellular adhesion molecule 1 (ICAM1) in meningeal vessels were tested to detect the vascular inflammation. Western blot was applied to measure the secretion of pro-inflammatory cytokines (IL-1β and IFNγ).ResultsOur results demonstrated that intraventricular injection of thrombin caused significant activation of meningeal macrophages, vascular inflammation, and neuron loss. Inhibition of PAR1 pathway attenuated the M1 polarization of meningeal macrophage, reduced the inflammatory infiltrations and prevented the neuron loss, as well as hydrocephalus after thrombin injection.ConclusionsClinically available PAR1 antagonists may offer a novel therapeutic approach candidate for the prevention or the management of inflammation in hemorrhage-induced hydrocephalus.

// Ling Peng 1, * , Yina Wang 1, * , Yun Hong 2 , Xianghua Ye 3 , Peng Shi 4, 5 , Junyan Zhang 6 and Qiong Zhao 1 1 Department of Thoracic Oncology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China 2 Department of Pharmacy, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China 3 Department of Radiotherapy, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China 4 Department of Medical Statistics, Children’s Hospital of Fudan University, Shanghai, China 5 Center for Evidence-based Medicine, Fudan University, Shanghai, China 6 Bothwin Clinical Study Consultant, Bellevue, WA, USA * These authors contributed equally to this work Correspondence to: Qiong Zhao, email: zhaoqiong@zju.edu.cn Keywords: BRAF inhibitor, MEK inhibitor, cuSCC, meta-analysis Received: July 25, 2017 Accepted: August 28, 2017 Published: September 19, 2017 ABSTRACT Background: BRAF inhibitor and dual BRAF/MEK inhibitors have been approved for the treatment of BRAF-mutated melanoma. Cutaneous squamous cell carcinoma (cuSCC) is an adverse event associated with these drugs. The contribution of BRAF inhibitor and dual BRAF/MEK inhibitors to cuSCC are still unknown. We performed this meta-analysis to determine the overall incidence and relative risk of cuSCC in cancer patients treated with these drugs. Results: A total of 7,442 patients from 24 primary studies were included. The incidences of all-grade and high-grade cuSCC in cancer patients treated with BRAF inhibitor were 12.5% (95% CI: 10.8–14.6%) and 11.6% (95% CI: 9.8–13.8%), and dual BRAF/MEK inhibitors were 3.0% (95% CI: 2.0–4.5%) and 2.8% (95% CI: 1.9–4.0%), respectively. On subgroup analysis and meta-regression, the incidence of cuSCC did not vary with tumor type, study design and specific drug used. The use of single agent BRAF inhibitor significantly increased the risk of developing cuSCC comparing with dual BRAF/MEK inhibitors for all-grade (RR 4.72, 95% CI: 2.42–9.20) and high-grade (RR 4.92, 95% CI: 2.64–9.16) in cancer patients. Materials and Methods: The databases of PubMed, Embase and abstracts published in ASCO proceedings were searched for relevant studies from January 2000 to June 2017. Summary incidences, relative risks (RRs) and 95% confidence intervals (CIs) were calculated by using either random effects or fixed effect models according to the heterogeneity of included studies. Conclusions: BRAF inhibitor significantly increases the risk of developing cuSCC compared with dual BRAF/MEK inhibitors in cancer patients. Clinicians should be aware of the risks of cuSCC with the administration of these drugs in cancer patients.

Aflibercept (Ziv-aflibercept, VEGF Trap, AVE005) is an engineered protein that functions as a decoy receptor to bind vascular endothelial growth factor A (VEGF-A). Hemorrhagic events, including epistaxis, gastrointestinal bleeding, and pulmonary bleeding, is one of its major adverse effects, but the incidence rate and overall risk has not been systematically studied. Therefore, we conducted a meta-analysis of published clinical trials to investigate the incidence and relative risk of hemorrhagic events in cancer patients treated with aflibercept. Electronic databases including PubMed, Embase, Cochrane databases, and American Society of Clinical Oncology abstracts were searched. Eligible studies were phase II and III prospective clinical trials of cancer patients treated with aflibercept with toxicity profile on hemorrhagic events. Overall incidence rates, relative risk (RR), and 95 % confidence intervals (CI) were calculated using fixed or random effects models depending on the heterogeneity of the included studies. A total of 4,538 patients with a variety of solid tumors from 13 prospective clinical trials were included for the meta-analysis. The overall incidences of all-grade and high-grade hemorrhagic events in cancer patients were 22.1 % (95 % CI, 16.5-29.7 %) and 4.2 % (95 % CI, 3.9-4.6 %), respectively. The relative risks of hemorrhagic events of aflibercept compared to control were increased for all-grade (RR = 2.63; 95 % CI, 2.07-3.34) and high-grade (RR = 2.45, 95 % CI, 1.62-3.72) hemorrhagic events. The risk of developing high-grade hemorrhagic events with aflibercept was comparable to that of bevacizumab (RR = 1.26; 95 % CI, 0.89-1.79). Aflibercept is associated with an increased risk of developing hemorrhagic events in patients with solid tumors. Close monitoring and management of hemorrhagic events are recommended.

Mammalian target of rapamycin (mTOR) inhibitors, everolimus and temsirolimus, are approved for the treatment of a variety of malignancies. Fatigue has been described with these agents as a common side effect, although the overall incidence and risk remain unclear. We performed a meta-analysis to calculate the overall incidence of fatigue in cancer patients treated with everolimus and temsirolimus and to compare the differences in incidence with placebo. The electronic databases PubMed, Embase, Web of Science, and Cochrane databases were searched for studies to include in the meta-analysis. Eligible studies were phase II and III prospective clinical trials of cancer patients treated with single drug everolimus or temsirolimus with toxicity data on fatigue. Overall incidence rates, relative risk (RR), and 95 % confidence intervals (CI) were calculated employing fixed or random effects models depending on the heterogeneity of the included studies. A total of 9,760 patients with a variety of malignancies from 56 prospective clinical trials were included for the meta-analysis. The overall incidences of all-grade and high-grade fatigue in cancer patients treated with mTOR inhibitor (everolimus or temsirolimus) were 45.4 % (95 % CI 36.9–55.8 %) and 8.7 % (95 % CI 7.2–10.4 %), respectively. The relative risks of fatigue of mTOR inhibitor compared to placebo were increased for all-grade (RR = 1.22, 95 % CI 1.08–1.38, P = 0.002) and high-grade (RR = 1.82, 95 % CI 1.24–2.69, P = 0.002) fatigue. The incidence of all-grade fatigue of patients treated with everolimus was higher than those with temsirolimus (RR = 1.85, 95 % CI 1.71–2.01, P < 0.001). No significant difference was detected with between everolimus and temsirolimus in terms of high-grade fatigue (RR = 1.15, 95 % CI 0.94–1.41, P = 0.18). Treatment with mTOR inhibitor, everolimus and temsirolimus, is associated with an increased incidence of fatigue in patients with cancer. Early detection and management of fatigue is needed.

Background Aflibercept is a human recombinant fusion protein with antiangiogenic effects that functions as a decoy receptor to bind vascular endothelial growth factor A. Proteinuria is one of its major adverse effects with a substantial variation in the incidence rate, and the overall risk of proteinuria has not been systematically studied. We performed a meta-analysis of published clinical trials to quantify the incidence and relative risk of proteinuria in cancer patients treated with aflibercept. Methods The electronic databases were searched, including PubMed, Embase, Cochrane databases, and ASCO (American Society of Clinical Oncology) abstracts. Eligible studies were phase II and III prospective clinical trials of cancer patients treated with aflibercept with toxicity data on proteinuria. Overall incidence rates, relative risk (RR), and 95% confidence intervals (CI) were calculated using fixed or random effects models depending on the heterogeneity of the included studies. Results A total of 4,596 patients with a variety of solid tumors from 16 prospective clinical trials were included for the meta-analysis. The overall incidences of all-grade and high-grade proteinuria in cancer patients were 33.9% (95% CI: 27.3–42.1%) and 7.9% (95% CI: 6.1–10.2%). The relative risks of proteinuria of aflibercept compared to control were increased for all-grade (RR = 1.41, 95% CI: 1.13–1.77) and high-grade (RR = 6.18, 95% CI: 3.78–10.12) proteinuria. The risk of developing all-grade and high-grade proteinuria with aflibercept was substantially higher than that of bevacizumab (all-grade: RR 1.85, 95% CI: 1.63–2.11; high-grade: RR 2.37, 95% CI: 1.84–3.05). Conclusions Aflibercept is associated with an increased risk of developing proteinuria. Appropriate monitoring and treatment is strongly recommended to prevent potential renal damage. Future studies are still needed to investigate the risk reduction and possible use of aflibercept in cancer patients.

In positron emission tomography, transmission scans can be performed to estimate attenuation correction factors (ACFs) which are in turn used to correct the emission scans. And such an attenuation correction is crucial for quantitatively accurate PET reconstructions. The prior model used in this work was based on our assumption that the attenuation values vary smoothly, with occasional discontinuities at anatomical borders. And on the other hand, long acquisition or scan times, although alleviating the noise effect of the count-limited scans, are blamed for patient uncomfortableness and movements. So, transmission tomography often suffers from the noise effect because of the short scan time. Thus reconstruction which is capable of overcoming the noise effect is highly needed. In this article, we apply the nonlocal prior Bayesian reconstruction method in PET transmission tomography. Resulting experimentations validate that the reconstructions using the nonlocal prior can reconstruct better transmission images and overcome noise effect even when the scan time is relatively short.