Dual-optimized adaptive Kalman filtering algorithm based on BP neural network and variance compensation for laser absorption spectroscopy.

A dual-optimized adaptive Kalman filtering (DO-AKF) algorithm based on back propagation (BP) neural network and variance compensation was developed for high-sensitivity trace gas detection in laser spectroscopy. The BP neural network was used to optimize the Kalman filter (KF) parameters. Variance compensation was introduced to track the state of the system and to eliminate the variations in the parameters of dynamic systems. The proposed DO-AKF algorithm showed the best performance compared with the traditional multi-signal average, extended KF, unscented KF, KF optimized by BP neural network (BP-KF) and KF optimized by variance compensation (VC-KF). The optimized DO-AKF algorithm was applied to a QCL-based gas sensor system for an exhaled CO analysis. The experimental results revealed a sensitivity enhancement factor of 23. The proposed algorithm can be widely used in the fields of environmental pollutant monitoring, industrial process control, and breath gas diagnosis.

Fast-track multidisciplinary treatment versus conventional treatment for colorectal cancer: a multicenter, open-label randomized controlled study.

BACKGROUND: Laparoscopic surgery, fast-track perioperative treatment and XELOX chemotherapy are effective strategies for shortening the duration of hospital stay for cancer patients. This trial aimed to clarify the safety and efficacy of the fast-track multidisciplinary treatment (FTMDT) model compared to conventional surgery combined with chemotherapy in Chinese colorectal cancer patients. METHODS: This trial was a prospective randomized controlled study with a 2 × 2 balanced factorial design and was conducted at six hospitals. Patients in group 1 (FTMDT) received fast-track perioperative treatment and XELOX adjuvant chemotherapy. Patients in group 2 (conventional treatment) received conventional perioperative treatment and mFOLFOX6 adjuvant chemotherapy. Subgroups 1a and 2a had laparoscopic surgery and subgroups 1b and 2b had open surgery. The primary endpoint was total length of hospital stay during treatment. RESULTS: A total of 374 patients were randomly assigned to the four subgroups, and 342 patients were finally analyzed, including 87 patients in subgroup 1a, 85 in subgroup 1b, 86 in subgroup 2a, and 84 in subgroup 2b. The total hospital stay of group 1 was shorter than that of group 2 [13 days, (IQR, 11-17 days) vs. 23.5 days (IQR, 15-42 days), P = 0.0001]. Compared to group 2, group 1 had lower surgical costs, fewer in-hospital complications and faster recovery (all P < 0.05). Subgroup 1a showed faster surgical recovery than that of subgroup 1b (all P < 0.05). There was no difference in 5-year overall survival between groups 1 and 2 [87.1% (95% CI, 80.7-91.5%) vs. 87.1% (95% CI, 80.8-91.4%), P = 0.7420]. CONCLUSIONS: The FTMDT model, which integrates laparoscopic surgery, fast-track treatment, and XELOX chemotherapy, was the superior model for enhancing the recovery of Chinese patients with colorectal cancer. TRIAL REGISTRATION: ClinicalTrials.gov: NCT01080547 , registered on March 4, 2010.

The role of multislice computed tomography of the costal cartilage in adult age estimation.

To establish population-specific age estimation models in adults from costal cartilage for contemporary Chinese by using three-dimensional volume-rendering technique. Five hundred and twelve individuals (254 females and 258 males) with documented ages between 20 and 85 years were retrospectively included. Their clinical CT examinations (1 mm slice thickness) were used to develop the sex-specific age prediction model. A validation sample comprising 26 female and 24 male individuals was then used to test the predictive accuracy of the established models. Simple linear regression (SLR), multiple linear regression (MLR), gradient boosting regression (GBR), support vector machine (SVM), and decision tree regression (DTR) were utilized to build the age diagnosis models from calibration samples. By comparison, the decision tree regression was the relatively more accurate age prediction model for male, with mean absolute error = 5.31 years, least absolute error = 0.10 years, correct percentage within 5 years = 54%, and the correct percentage within 10 years = 88%. The stepwise multiple linear regression equations was the relatively more accurate one for female, with mean absolute error = 6.72 years, least absolute error = 0.68 years, correct percentage within 5 years = 42%, and correct percentage within 10 years = 77%. Our results indicated that the present established age estimation model can be applied as an additional guidance for age estimation in adults.

Intradialytic blood pressure pattern recognition based on density peak clustering.

End-stage renal disease (ESRD) is the final stage of chronic kidney disease (CKD) and requires hemodialysis (HD) for survival. Intradialytic blood pressure (IBP) measurements are necessary to ensure patient safety during HD treatments and have critical clinical and prognostic significance. Studies on IBP measurements, especially IBP patterns, are limited. All related studies have been based on a priori knowledge and artificially classified IBP patterns. Therefore, the results were influenced by subjective concepts. In this study, we proposed a new approach to identify IBP patterns to classify ESRD patients. We used the dynamic time warping (DTW) algorithm to measure the similarity between two series of IBP data. Five blood pressure (BP) patterns were identified by applying the density peak clustering algorithm (DPCA) to the IBP data. To illustrate the association between BP patterns and prognosis, we constructed three random survival forest (RSF) models with different covariates. Model accuracy was improved 3.7-6.3% by the inclusion of BP patterns. The results suggest that BP patterns have critical clinical and prognostic significance regarding the risk of cerebrovascular events. We can also apply this clustering approach to other time series data from electronic health records (EHRs). This work is generalizable to analyses of dense EHR data.

POPCORN: A web service for individual PrognOsis prediction based on multi-center clinical data CollabORatioN without patient-level data sharing.

BACKGROUND AND OBJECTIVE: Clinical prognosis prediction plays an important role in clinical research and practice. The construction of prediction models based on electronic health record data has recently become a research focus. Due to the lack of external validation, prediction models based on single-center, hospital-specific datasets may not perform well with datasets from other medical institutions. Therefore, research investigating prognosis prediction model construction based on a collaborative analysis of multi-center electronic health record data could increase the number and coverage of patients used for model training, enrich patient prognostic features and ultimately improve the accuracy and generalization of prognosis prediction. MATERIALS AND METHODS: A web service for individual prognosis prediction based on multi-center clinical data collaboration without patient-level data sharing (POPCORN) was proposed. POPCORN focuses on solving key issues in multi-center collaborative research based on electronic health record systems; these issues include the standardization of clinical data expression, the preservation of patient privacy during model training and the effect of case mix variance on the prediction model construction and application. POPCORN is based on a multivariable meta-analysis and a Bayesian framework and can construct suitable prediction models for multiple clinical scenarios that can effectively adapt to complex clinical application environments. RESULTS: POPCORN was validated using a joint, multi-center collaborative research network between China and the United States with patients diagnosed with colorectal cancer. The performance of the models based on POPCORN was comparable to that of the standard prognosis prediction model; however, POPCORN did not expose raw patient data. The prediction models had similar AUC, but the BMA model had the lowest ECI across all prediction models, indicating that this model had better calibration performance than the other models, especially for patients in Chinese hospitals. CONCLUSIONS: The POPCORN system can build prediction models that perform well in complex clinical application scenarios and can provide effective decision support for individual patient prognostic predictions.

An ontology-based approach to patient follow-up assessment for continuous and personalized chronic disease management.

OBJECTIVE: Chronic diseases are complex and persistent clinical conditions that require close collaboration among patients and health care providers in the implementation of long-term and integrated care programs. However, current solutions focus partially on intensive interventions at hospitals rather than on continuous and personalized chronic disease management. This study aims to fill this gap by providing computerized clinical decision support during follow-up assessments of chronically ill patients at home. METHODS: We proposed an ontology-based framework to integrate patient data, medical domain knowledge, and patient assessment criteria for chronic disease patient follow-up assessments. A clinical decision support system was developed to implement this framework for automatic selection and adaptation of standard assessment protocols to suit patient personal conditions. We evaluated our method in the case study of type 2 diabetic patient follow-up assessments. RESULTS: The proposed framework was instantiated using real data from 115,477 follow-up assessment records of 36,162 type 2 diabetic patients. Standard evaluation criteria were automatically selected and adapted to the particularities of each patient. Assessment results were generated as a general typing of patient overall condition and detailed scoring for each criterion, providing important indicators to the case manager about possible inappropriate judgments, in addition to raising patient awareness of their disease control outcomes. Using historical data as the gold standard, our system achieved a rate of accuracy of 99.93% and completeness of 95.00%. CONCLUSIONS: This study contributes to improving the accessibility, efficiency and quality of current patient follow-up services. It also provides a generic approach to knowledge sharing and reuse for patient-centered chronic disease management.

14-3-3ζ promotes lung cancer cell invasion by increasing the Snail protein expression through atypical protein kinase C (aPKC)/NF-κB signaling.

14-3-3ζ has been identified as a putative oncogene in several cancers, including non-small cell lung cancer (NSCLC). However, the mechanisms underlying its functions remain undefined. In this study, we show that overexpression of 14-3-3ζ was frequently detected in lung adenocarcinoma (LuAC) tissues and was significantly associated with lymph node metastasis and poor outcome. Functional studies demonstrated that 14-3-3ζ promoted migration and invasion in A549 cells, both of which were effectively inhibited when 14-3-3ζ was silenced with short hairpin RNA (shRNA). Furthermore, 14-3-3ζ-mediated invasion of cancer cells was found to upregulate Snail through the activation of atypical protein kinase C (aPKC). Activation of aPKCζ mediates this effect by stimulating NF-κB signaling. Our results identify a specific pathway by which 14-3-3ζ induces tumor invasion and provide insight into potential therapeutic approaches to target 14-3-3ζ-associated lung adenocarcinoma.

The tumor suppressive role of RASSF1A in osteosarcoma through the Wnt signaling pathway.

Ras-association domain family 1 isoform A (RASSF1A) is a tumor suppressor gene and its expression is lost in numerous types of cancer cells, including primary osteosarcoma cells. However, its functional significance in osteosarcoma has not been well defined. The messenger RNA (mRNA) expression of RASSF1A in osteosarcoma tissues and corresponding non-tumoral tissues was measured by real-time PCR. Overexpression of RASSF1A was established by an adenoviral vector expressing RASSF1A. Cell migration and invasion were analyzed in transwells. Apoptosis and cell cycle were analyzed using flow cytometry. Wnt/ß-catenin activity was measured by TCF reporter dual-luciferase assay. Cell viability was measured by MTT assay. Protein expression was detected by Western blot. RASSF1A mRNA expression was significantly lower in osteosarcoma tissues than that in the corresponding non-tumoral tissues. The lowered RASSF1A expression correlated with the clinical severity of osteosarcoma. rAd-RASSF1A injection significantly inhibited the growth of xenograft MNNG/HOS tumors in mice. Overexpression of RASSF1A resulted in significant inhibition of the proliferation, migration, and invasion; induced apoptosis; and arrested cell cycle at G0/G1 phase in both the MNNG/HOS and SaOS2 cells. Overexpression of RASSF1A inhibited the Wnt/ß-catenin activity, decreased phosphorylation of Akt/glycogen synthase kinase-3-ß (GSK3-ß), and increased phosphorylation of mammalian sterile 20-like kinase 1 (MST1). Overexpression of RASSF1A downregulated the cyclin D1, c-Myc, and matrix metalloproteinase-7 (MMP-7) protein levels. RASSF1A functions as a tumor suppressor in osteosarcoma and exerts anti-cancer roles through regulating Akt/GSK-3-Wnt/ß-catenin signaling.

Design and Development of a Sharable Clinical Decision Support System Based on a Semantic Web Service Framework.

Clinical decision support (CDS) systems provide clinicians and other health care stakeholders with patient-specific assessments or recommendations to aid in the clinical decision-making process. Despite their demonstrated potential for improving health care quality, the widespread availability of CDS systems has been limited mainly by the difficulty and cost of sharing CDS knowledge among heterogeneous healthcare information systems. The purpose of this study was to design and develop a sharable clinical decision support (S-CDS) system that meets this challenge. The fundamental knowledge base consists of independent and reusable knowledge modules (KMs) to meet core CDS needs, wherein each KM is semantically well defined based on the standard information model, terminologies, and representation formalisms. A semantic web service framework was developed to identify, access, and leverage these KMs across diverse CDS applications and care settings. The S-CDS system has been validated in two distinct client CDS applications. Model-level evaluation results confirmed coherent knowledge representation. Application-level evaluation results reached an overall accuracy of 98.66 % and a completeness of 96.98 %. The evaluation results demonstrated the technical feasibility and application prospect of our approach. Compared with other CDS engineering efforts, our approach facilitates system development and implementation and improves system maintainability, scalability and efficiency, which contribute to the widespread adoption of effective CDS within the healthcare domain.

An electronic medical record system with treatment recommendations based on patient similarity.

As the core of health information technology (HIT), electronic medical record (EMR) systems have been changing to meet health care demands. To construct a new-generation EMR system framework with the capability of self-learning and real-time feedback, thus adding intelligence to the EMR system itself, this paper proposed a novel EMR system framework by constructing a direct pathway between the EMR workflow and EMR data. A prototype of this framework was implemented based on patient similarity learning. Patient diagnoses, demographic data, vital signs and structured lab test results were considered for similarity calculations. Real hospitalization data from 12,818 patients were substituted, and Precision @ Position measurements were used to validate self-learning performance. Our EMR system changed the way in which orders are placed by establishing recommendation order menu and shortcut applications. Two learning modes (EASY MODE and COMPLEX MODE) were provided, and the precision values @ position 5 of both modes were 0.7458 and 0.8792, respectively. The precision performance of COMPLEX MODE was better than that of EASY MODE (tested using a paired Wilcoxon-Mann-Whitney test, p < 0.001). Applying the proposed framework, the EMR data value was directly demonstrated in the clinical workflow, and intelligence was added to the EMR system, which could improve system usability, reliability and the physician's work efficiency. This self-learning mechanism is based on dynamic learning models and is not limited to a specific disease or clinical scenario, thus decreasing maintenance costs in real world applications and increasing its adaptability.

Research and Development of Semantics-based Sharable Clinical Pathway Systems.

The clinical pathway (CP) as a novel medical management schema is beneficial for reducing the length of stay, decreasing heath care costs, standardizing clinical activities, and improving medical quality. However, the practicability of CPs is limited by the complexity and expense of adding the standard functions of electronic CPs to existing electronic medical record (EMR) systems. The purpose of this study was to design and develop an independent clinical pathway (ICP) system that is sharable with different EMR systems. An innovative knowledge base pattern was designed with separate namespaces for global knowledge, local knowledge, and real-time instances. Semantic web technologies were introduced to support knowledge sharing and intelligent reasoning. The proposed system, which was developed in a Java integrated development environment, achieved standard functions of electronic CPs without modifying existing EMR systems and integration environments in hospitals. The interaction solution between the pathway system and the EMR system simplifies the integration procedures with other hospital information systems. Five categories of transmission information were summarized to ensure the interaction process. Detailed procedures for the application of CPs to patients and managing exceptional alerts are presented by explicit data flow analysis. Compared to embedded pathway systems, independent pathway systems feature greater feasibility and practicability and are more advantageous for achieving the normalized management of standard CPs.

Design and development of a medical big data processing system based on Hadoop.

Secondary use of medical big data is increasingly popular in healthcare services and clinical research. Understanding the logic behind medical big data demonstrates tendencies in hospital information technology and shows great significance for hospital information systems that are designing and expanding services. Big data has four characteristics--Volume, Variety, Velocity and Value (the 4 Vs)--that make traditional systems incapable of processing these data using standalones. Apache Hadoop MapReduce is a promising software framework for developing applications that process vast amounts of data in parallel with large clusters of commodity hardware in a reliable, fault-tolerant manner. With the Hadoop framework and MapReduce application program interface (API), we can more easily develop our own MapReduce applications to run on a Hadoop framework that can scale up from a single node to thousands of machines. This paper investigates a practical case of a Hadoop-based medical big data processing system. We developed this system to intelligently process medical big data and uncover some features of hospital information system user behaviors. This paper studies user behaviors regarding various data produced by different hospital information systems for daily work. In this paper, we also built a five-node Hadoop cluster to execute distributed MapReduce algorithms. Our distributed algorithms show promise in facilitating efficient data processing with medical big data in healthcare services and clinical research compared with single nodes. Additionally, with medical big data analytics, we can design our hospital information systems to be much more intelligent and easier to use by making personalized recommendations.

Creating hospital-specific customized clinical pathways by applying semantic reasoning to clinical data.

OBJECTIVE: Clinical pathways (CPs) are widely studied methods to standardize clinical intervention and improve medical quality. However, standard care plans defined in current CPs are too general to execute in a practical healthcare environment. The purpose of this study was to create hospital-specific personalized CPs by explicitly expressing and replenishing the general knowledge of CPs by applying semantic analysis and reasoning to historical clinical data. METHODS: A semantic data model was constructed to semantically store clinical data. After querying semantic clinical data, treatment procedures were extracted. Four properties were self-defined for local ontology construction and semantic transformation, and three Jena rules were proposed to achieve error correction and pathway order recognition. Semantic reasoning was utilized to establish the relationship between data orders and pathway orders. RESULTS: A clinical pathway for deviated nasal septum was used as an example to illustrate how to combine standard care plans and practical treatment procedures. A group of 224 patients with 11,473 orders was transformed to a semantic data model, which was stored in RDF format. Long term order processing and error correction made the treatment procedures more consistent with clinical practice. The percentage of each pathway order with different probabilities was calculated to declare the commonality between the standard care plans and practical treatment procedures. Detailed treatment procedures with pathway orders, deduced pathway orders, and orders with probability greater than 80% were provided to efficiently customize the CPs. CONCLUSIONS: This study contributes to the practical application of pathway specifications recommended by the Ministry of Health of China and provides a generic framework for the hospital-specific customization of standard care plans defined by CPs or clinical guidelines.

Use of an agent-based simulation model to evaluate a mobile-based system for supporting emergency evacuation decision making.

Recently, mass casualty incidents (MCIs) have been occurring frequently and have gained international attention. There is an urgent need for scientifically proven and effective emergency responses to MCIs, particularly as the severity of incidents is continuously increasing. The emergency response to MCIs is a multi-dimensional and multi-participant dynamic process that changes in real-time. The evacuation decisions that assign casualties to different hospitals in a region are very important and impact both the results of emergency treatment and the efficiency of medical resource utilization. Previously, decisions related to casualty evacuation were made by an incident commander with emergency experience and in accordance with macro emergency guidelines. There are few decision-supporting tools available to reduce the difficulty and psychological pressure associated with the evacuation decisions an incident commander must make. In this study, we have designed a mobile-based system to collect medical and temporal data produced during an emergency response to an MCI. Using this information, our system's decision-making model can provide personal evacuation suggestions that improve the overall outcome of an emergency response. The effectiveness of our system in reducing overall mortality has been validated by an agent-based simulation model established to simulate an emergency response to an MCI.

Design and development of a mobile-based system for supporting emergency triage decision making.

Emergency care for mass casualty incidents is a sophisticated multi-participant process. To manage this process effectively, many information systems have been proposed. However, their performance in improving the efficiency and accuracy of patient triage is not satisfactory. This paper is concerned with the development of a mobile-based system for supporting emergency triage in the emergency care process for mass casualty incidents. This system collects the patient's emergency data throughout the whole emergency care process through a mobile application and data transfer mechanism. Using a Cox proportional hazard model, the system has the capacity to present the survival curve to the triage officer, helping him/her to make triage and transportation decisions. This system offers an alternative injury assessment tool based on the vital signs data of the injury patient. With the help of this system, the triage officer can more directly and comprehensively learn about each patient's situation and deterioration without additional operations at the incident site.

Cloud-based hospital information system as a service for grassroots healthcare institutions.

Grassroots healthcare institutions (GHIs) are the smallest administrative levels of medical institutions, where most patients access health services. The latest report from the National Bureau of Statistics of China showed that 96.04 % of 950,297 medical institutions in China were at the grassroots level in 2012, including county-level hospitals, township central hospitals, community health service centers, and rural clinics. In developing countries, these institutions are facing challenges involving a shortage of funds and talent, inconsistent medical standards, inefficient information sharing, and difficulties in management during the adoption of health information technologies (HIT). Because of the necessity and gravity for GHIs, our aim is to provide hospital information services for GHIs using Cloud computing technologies and service modes. In this medical scenario, the computing resources are pooled by means of a Cloud-based Virtual Desktop Infrastructure (VDI) to serve multiple GHIs, with different hospital information systems dynamically assigned and reassigned according to demand. This paper is concerned with establishing a Cloud-based Hospital Information Service Center to provide hospital information software as a service (HI-SaaS) with the aim of providing GHIs with an attractive and high-performance medical information service. Compared with individually establishing all hospital information systems, this approach is more cost-effective and affordable for GHIs and does not compromise HIT performance.

The meaningful use of EMR in Chinese hospitals: a case study on curbing antibiotic abuse.

Comprehensive electronic medical records (EMR) have proven to be an effective tool for improving the safety and quality of healthcare. In particular, EMR has resulted in a significant reduction in antibiotic consumption and an improvement in rational antibiotic use. This study focused on evaluating the effectiveness of using a comprehensive EMR system to curb antibiotic abuse. A retrospective "before/after" investigation was conducted in an 850-bed nonprofit hospital. A comprehensive EMR was implemented to improve the management of medical practices during treatment and to track antibiotic prescriptions. Patient records on antibiotic use were identified by searching the hospital database from April 1 to October 30, 2011 (post-implementation) and compared with those of patients from April 1 to October 30, 2010 (pre-implementation). Outcome measures were defined daily doses (DDDs) of antibiotics per 1,000 patient-days and factors that influenced antibiotic consumption. Antibiotic consumption during the seven months with EMR management decreased from 727 DDDs to 480 DDDs per 1,000 patient-days (p < 0.001). The decreases in cefminox, cefoperazone combinations, cefixime, and gentamicin were notably significant. The type of medical insurance, patient age, and ordering department (importance coefficients of 0.1935, 0.1818 and 0.1756, respectively) were the most significant factors that influenced antibiotic consumption. An improvement in the rational antibiotic use was also observed in the length of antibiotic therapy, selection of appropriate antibiotic level, route of administration, and so on. Rational antibiotic use resulted in a lower mortality of 0.0644 % during the post-implementation period compared to 0.179 % during the pre-implementation period (p = 0.018). The comprehensive EMR system contributed to a significant reduction in antibiotic consumption and an improvement in rational antibiotic use.

Surgical management for multilevel noncontiguous thoracic spinal tuberculosis by single-stage posterior transforaminal thoracic debridement, limited decompression, interbody fusion, and posterior instrumentation (modified TTIF).

PURPOSE: Multilevel noncontiguous thoracic spinal tuberculosis has rarely been reported in the literature. We present a retrospective clinical study of 14 patients with multilevel noncontiguous thoracic spinal tuberculosis treated by single-stage posterior transforaminal thoracic debridement, limited decompression, interbody fusion, and posterior instrumentation (modified TTIF) and determine the clinical effectiveness of such surgical treatment for MNTST. METHODS: Fourteen patients with multilevel noncontiguous thoracic spinal tuberculosis were treated with modified TTIF. The mean follow-up was 27.36 ± 10.46 months (range 13-42 months). The kyphotic angle ranged from -2° to 47° before operation, with an average of 19.21° ± 12.63°. The erythrocyte sedimentation rate (ESR) of patients upon admission ranged from 30 to 62 mm/h before operation, with an average of 46.43 ± 10.77 mm/h. The Frankel Grade was used to evaluate the neurological deficits. RESULTS: The average ESR got normal (8.14 ± 5.89 mm/h) within 3 months in all patients. The average kyphotic angle decreased to 8.07° ± 6.91° postoperatively. Mean deformity angle was measured as 8.79° ± 7.29° at the last visit. Solid fusion was achieved in all cases. Neurologic status of the 12 patients with preoperative neurologic deficit was 6 with grade D recovered to normal; 2 with grade B, both of them to grade D; 4 with grade C, 2 to grade D, 1 to grade E, and 1 still in grade C. CONCLUSIONS: Modified TTIF can be an effective treatment method of multilevel noncontiguous thoracic spinal tuberculosis.

Real-World Survival Outcomes Based on EGFR Mutation Status in Chinese Patients With Lung Adenocarcinoma After Complete Resection: Results From the ICAN Study.

INTRODUCTION: The adjuvant treatment of patients with resected lung adenocarcinoma (LUAD) remains unstandardized. We analyzed the survival outcomes of these patients based on EGFR mutation status and adjuvant chemotherapy treatment. METHODS: This noninterventional real-world study (ICAN) enrolled Chinese patients with resected stages I to III LUAD from April 8, 2010, to December 31, 2010. Tumor EGFR mutation status and 3-year disease-free survival (DFS) were determined. The extension phase provided long-term follow-up with overall survival (OS) as the primary end point. Secondary end points included DFS and prognostic factors of survival. Survival outcomes based on adjuvant chemotherapy treatment, EGFR mutation status, and postoperative stage were analyzed post hoc. RESULTS: Among 568 patients in the ICAN cohort, 472 continued to the extension phase and remained eligible. The 3-year DFS rate was 58.8%. In the extension cohort, 260 patients (55.1%) had EGFR-mutant disease and 207 (43.9%) received adjuvant chemotherapy. At a median follow-up of 109.0 (95% confidence interval [CI]: 106.6-111.4) months, median OS and DFS were 103.3 (95% CI: 101.7-104.9) and 67.4 (95% CI: 49.7-85.2) months, respectively. The 5-year OS and DFS rates were 68.9% (95% CI: 64.3-73.6) and 52.9% (95% CI: 48.2-57.7), respectively. EGFR wild-type disease was a significant independent predictor of worse OS (HR = 1.24, 95% CI: 1.07-1.44, p= 0.004) based on the Cox regression analysis of common factors. Post hoc subgroup analysis revealed that survival outcomes were not significantly different with adjuvant chemotherapy regardless of EGFR mutation status across all postoperative stages. CONCLUSIONS: EGFR mutations are common in operable LUAD, and recurrence and mortality after resection were considerable. Adjuvant chemotherapy did not improve survival outcomes, regardless of EGFR mutation status and postoperative stage.

The Clinicopathological Characteristics of Alpha-Fetoprotein-Producing Adenocarcinoma of the Gastrointestinal Tract-A Single-Center Retrospective Study.

Alpha-fetoprotein (AFP)-producing adenocarcinoma from the gastrointestinal tract (APA-GI) is a rare type of highly malignant tumor with a poor prognosis. It may originate from any site along the GI tract with similar clinicopathological characteristics. As limited research had ever described the characteristics of APA-GI, the present article intends to systemically investigate the clinicopathological characteristics of APA-GI from a single center's retrospective study to deepen the understanding of the disease. A total of 177 patients pathologically diagnosed with APA-GI between 2010 and 2017 at the Second Affiliated Hospital of Zhejiang University, School of Medicine, were included. Also, clinical data of 419 gastric cancers and 609 colorectal cancers from The Cancer Genome Atlas database were also extracted. Clinical information of patients from Second Affiliated Hospital of Zhejiang University, School of Medicine, was collected, and a median follow-up of 14.5 months was performed to investigate clinical characteristics of APA-GI. For the pathological characteristics of APA-GI, hematoxylin-eosin sections were reviewed, and immunohistochemistry of AFP was performed. The results showed that the primary tumor could develop through the whole GI tract, including the esophagus (0.6%), stomach (83.1%), duodenum (1.1%), ileum (0.6%), appendix (0.6%), colon (5.1%), and rectum (7.9%). Hepatoid adenocarcinoma is the main pathological feature of APA-GI. AFP expression level in tumor tissue was not strictly associated with serum AFP or hepatoid differentiation. The prognosis of APA-GI was worse than that of common adenocarcinoma of the GI tract and liver metastasis, and high AFP levels suggest poor prognosis in patients with APA-GI. Therefore, the present study was the first research to systemically explore the clinicopathological characteristics of APA-GI. APA-GI occurs through the whole GI tract with a significantly worse prognosis than common adenocarcinoma of GI. APA-GI should be regarded as one kind of disease for its similar clinicopathological characteristics within patients.