Outcomes of local thoracic surgery in patients with stage IV non-small-cell lung cancer: A SEER-based analysis.

BACKGROUND: The outcomes of thoracic surgery for patients with stage IV non-small-cell lung cancer (NSCLC) are controversial and uncertain. PATIENTS AND METHODS: The National Cancer Institute's Surveillance, Epidemiology, and End Results was queried for patients with stage IV NSCLC, including those treated with surgery-participated therapy modalities. Overall survival (OS) was evaluated using a variety of statistical analyses. RESULTS: The analysis was carried out for 90,982 patients from 1975 to 2016 who had been diagnosed as stage IV NSCLC. Propensity score-matched (PSM) analyses that were well-balanced with all the important confounding covariates revealed improved OS (median survival time [MST]) with patients receiving surgery versus non-surgery (MST: 15 versus 8 months, P < 0.001); undergoing surgery plus chemotherapy versus chemotherapy (MST: 19 versus 11 months, P < 0.001); and having surgery plus chemoradiation versus chemoradiation (MST: 18 versus 11 months, P < 0.001). Sequential landmark analyses for long-term survivors of ≥1 and ≥3 years all indicated improved OS (P < 0.001) on univariate and multivariate analyses for the patients receiving the three surgery-related treatment patterns listed earlier, relative to the corresponding surgery-absent treatment modalities. For synchronous presentations of varied treatment paradigms, surgical intervention significantly led to increased OS (MST, months) benefits following treatment paradigms: surgery plus chemotherapy (22), surgery plus chemoradiation (18), chemotherapy (10), surgery only (9), chemoradiation (9), surgery plus radiation (6) and radiation alone (2). The subgroup analysis demonstrated that the elevated OS associated with local thoracic surgery in addition to chemotherapy (versus chemotherapy) or chemoradiation (versus chemoradiation) fell in the subcategories of T0-3, N0-2 and 0-1 (metastatic sites) tumours. The comparison of the aforementioned two types of treatment patterns indicated that the optimal patients for the surgery were those with any combination of T1-4, N0-3, Msite0-1 and adeno- or squamous carcinoma. CONCLUSIONS: The patients with T1-4, N0-3, Msite0-1 and adeno- or squamous carcinoma of stage IV NSCLC had a longer OS with local thoracic surgery in combination with chemotherapy or chemoradiation.

An efficient, large-scale, non-lattice-detection algorithm for exhaustive structural auditing of biomedical ontologies.

One of the basic challenges in developing structural methods for systematic audition on the quality of biomedical ontologies is the computational cost usually involved in exhaustive sub-graph analysis. We introduce ANT-LCA, a new algorithm for computing all non-trivial lowest common ancestors (LCA) of each pair of concepts in the hierarchical order induced by an ontology. The computation of LCA is a fundamental step for non-lattice approach for ontology quality assurance. Distinct from existing approaches, ANT-LCA only computes LCAs for non-trivial pairs, those having at least one common ancestor. To skip all trivial pairs that may be of no practical interest, ANT-LCA employs a simple but innovative algorithmic strategy combining topological order and dynamic programming to keep track of non-trivial pairs. We provide correctness proofs and demonstrate a substantial reduction in computational time for two largest biomedical ontologies: SNOMED CT and Gene Ontology (GO). ANT-LCA achieved an average computation time of 30 and 3 sec per version for SNOMED CT and GO, respectively, about 2 orders of magnitude faster than the best known approaches. Our algorithm overcomes a fundamental computational barrier in sub-graph based structural analysis of large ontological systems. It enables the implementation of a new breed of structural auditing methods that not only identifies potential problematic areas, but also automatically suggests changes to fix the issues. Such structural auditing methods can lead to more effective tools supporting ontology quality assurance work.

FEDRR: fast, exhaustive detection of redundant hierarchical relations for quality improvement of large biomedical ontologies.

BACKGROUND: Redundant hierarchical relations refer to such patterns as two paths from one concept to another, one with length one (direct) and the other with length greater than one (indirect). Each redundant relation represents a possibly unintended defect that needs to be corrected in the ontology quality assurance process. Detecting and eliminating redundant relations would help improve the results of all methods relying on the relevant ontological systems as knowledge source, such as the computation of semantic distance between concepts and for ontology matching and alignment. RESULTS: This paper introduces a novel and scalable approach, called FEDRR - Fast, Exhaustive Detection of Redundant Relations - for quality assurance work during ontological evolution. FEDRR combines the algorithm ideas of Dynamic Programming with Topological Sort, for exhaustive mining of all redundant hierarchical relations in ontological hierarchies, in O(c·|V|+|E|) time, where |V| is the number of concepts, |E| is the number of the relations, and c is a constant in practice. Using FEDRR, we performed exhaustive search of all redundant is-a relations in two of the largest ontological systems in biomedicine: SNOMED CT and Gene Ontology (GO). 372 and 1609 redundant is-a relations were found in the 2015-09-01 version of SNOMED CT and 2015-05-01 version of GO, respectively. We have also performed FEDRR on over 190 source vocabularies in the UMLS - a large integrated repository of biomedical ontologies, and identified six sources containing redundant is-a relations. Randomly generated ontologies have also been used to further validate the efficiency of FEDRR. CONCLUSIONS: FEDRR provides a generally applicable, effective tool for systematic detecting redundant relations in large ontological systems for quality improvement.

NHash: Randomized N-Gram Hashing for Distributed Generation of Validatable Unique Study Identifiers in Multicenter Research.

BACKGROUND: A unique study identifier serves as a key for linking research data about a study subject without revealing protected health information in the identifier. While sufficient for single-site and limited-scale studies, the use of common unique study identifiers has several drawbacks for large multicenter studies, where thousands of research participants may be recruited from multiple sites. An important property of study identifiers is error tolerance (or validatable), in that inadvertent editing mistakes during their transmission and use will most likely result in invalid study identifiers. OBJECTIVE: This paper introduces a novel method called "Randomized N-gram Hashing (NHash)," for generating unique study identifiers in a distributed and validatable fashion, in multicenter research. NHash has a unique set of properties: (1) it is a pseudonym serving the purpose of linking research data about a study participant for research purposes; (2) it can be generated automatically in a completely distributed fashion with virtually no risk for identifier collision; (3) it incorporates a set of cryptographic hash functions based on N-grams, with a combination of additional encryption techniques such as a shift cipher; (d) it is validatable (error tolerant) in the sense that inadvertent edit errors will mostly result in invalid identifiers. METHODS: NHash consists of 2 phases. First, an intermediate string using randomized N-gram hashing is generated. This string consists of a collection of N-gram hashes f1, f2, ..., fk. The input for each function fi has 3 components: a random number r, an integer n, and input data m. The result, fi(r, n, m), is an n-gram of m with a starting position s, which is computed as (r mod |m|), where |m| represents the length of m. The output for Step 1 is the concatenation of the sequence f1(r1, n1, m1), f2(r2, n2, m2), ..., fk(rk, nk, mk). In the second phase, the intermediate string generated in Phase 1 is encrypted using techniques such as shift cipher. The result of the encryption, concatenated with the random number r, is the final NHash study identifier. RESULTS: We performed experiments using a large synthesized dataset comparing NHash with random strings, and demonstrated neglegible probability for collision. We implemented NHash for the Center for SUDEP Research (CSR), a National Institute for Neurological Disorders and Stroke-funded Center Without Walls for Collaborative Research in the Epilepsies. This multicenter collaboration involves 14 institutions across the United States and Europe, bringing together extensive and diverse expertise to understand sudden unexpected death in epilepsy patients (SUDEP). CONCLUSIONS: The CSR Data Repository has successfully used NHash to link deidentified multimodal clinical data collected in participating CSR institutions, meeting all desired objectives of NHash.

[Comparison of clinicopathological features and prognosis in familial and sporadic gastric cancer].

OBJECTIVE: To analyze the clinicopathological characteristics and prognosis of familial gastric cancer and to improve the treatment outcome. METHODS: Clinical data of 67 patients with familial gastric cancer and 820 patients with sporadic gastric cancer in the Second Affiliated Hospital of Dalian Medical University from 1995 to 2005 were retrospectively analyzed. RESULTS: Compared to sporadic gastric cancer, the percentage of familial gastric cancer patients less than 45 years old was higher (34.3% vs. 14.6%). Early gastric cancer(23.9% vs. 13.8%), diffuse gastric cancer(79.1% vs. 29.0%), and lymph node metastasis (91.0% vs. 70.9%) were more common in patients with familial cancer(P<0.05). The 5-year survival rate of familial gastric cancer patients was lower than that of patients with sporadic gastric cancer(20.5% vs. 45.1%)(P<0.05). CONCLUSIONS: Familial gastric cancer has characteristics of younger onset age, advanced disease staging, higher positive lymph node ratio and poorer prognosis. Therefore, early diagnosis should be emphasized in the management of familial gastric cancer.