BACKGROUND Natural killer (NK) cells have a spontaneous cytotoxic capacity against tumor cells. These cells represent a small proportion of human colon carcinoma‐infiltrating lymphocytes. Their prognostic significance in these tumors has yet to be determined. METHODS One hundred and fifty‐seven patients who each had a colectomy for large bowel adenocarcinoma were studied. No patient received adjuvant therapy. Immunohistochemical stains were performed for NK cells using the monoclonal antibody CD57. The number of NK cells was counted using a MICRON image analyzer. The total area studied for each tumor was 1 cm2. In this area, 50 intratumoral fields of 0.173 mm2 were selected. The degree of NK infiltration was classified as little (<50 NK cells), moderate (50‐150 NK cells), and extensive (>150 NK cells). The Kaplan‐Meier method was used to obtain survival figures. Multivariate analyses were performed using the Cox regression model. RESULTS At 5 years, patients with little and moderate NK infiltration showed significantly shorter survival rates (overall and disease free survival) than those with extensive infiltration (P < 0.01). Three significant factors affecting survival were selected in a stepwise fashion in increasing order as follows: TNM stage, NK infiltration, and lymphocytic infiltration. Patients with TNM Stage III disease and extensive NK infiltration showed significantly longer survival rates than those with little or moderate infiltration (P < 0.001). In these patients, multivariate analysis using the Cox regression model identified two significant variables: number of involved lymph nodes and NK cell infiltration. CONCLUSIONS In patients with colorectal carcinoma, an extensive intratumoral infiltration of NK cells is associated with a favorable tumor outcome. Intratumoral infiltration of NK cells can be used as a variable with prognostic value, especially in patients with TNM Stage III disease. Cancer 1997; 79:2320‐8. © 1997 American Cancer Society.

Abstract. Discharge summaries and other free-text reports in healthcare transfer information between working shifts and geographic locations. Patients are likely to have difficulties in understanding their content, because of their medical jargon, non-standard abbreviations, and ward-specific idioms. This paper reports on an evaluation lab with an aim to support the continuum of care by developing methods and resources that make clinical reports in English easier to understand for patients, and which helps them in finding information related to their condition. This ShARe/CLEFeHealth2013 lab offered student mentoring and shared tasks: identification and normalisation of disorders (1a and 1b) and normalisation of abbreviations and acronyms (2) Overview of the ShARe/CLEF eHealth Evaluation Lab 2013 213 reports with respect to terminology standards in healthcare as well as information retrieval (3) to address questions patients may have when reading clinical reports. The focus on patients' information needs as opposed to the specialised information needs of physicians and other healthcare workers was the main feature of the lab distinguishing it from previous shared tasks. De-identified clinical reports for the three tasks were from US intensive care and originated from the MIMIC II database. Other text documents for Task 3 were from the Internet and originated from the Khresmoi project. Task 1 annotations originated from the ShARe annotations. For Tasks 2 and 3, new annotations, queries, and relevance assessments were created. 64, 56, and 55 people registered their interest in Tasks 1, 2, and 3, respectively. 34 unique teams (3 members per team on average) participated with 22, 17, 5, and 9 teams in Tasks 1a, 1b, 2 and 3, respectively. The teams were from Australia, China, France, India, Ireland, Republic of Korea, Spain, UK, and USA. Some teams developed and used additional annotations, but this strategy contributed to the system performance only in Task 2. The best systems had the F1 score of 0.75 in Task 1a; Accuracies of 0.59 and 0.72 in Tasks 1b and 2; and Precision at 10 of 0.52 in Task 3. The results demonstrate the substantial community interest and capabilities of these systems in making clinical reports easier to understand for patients. The organisers have made data and tools available for future research and development.

AimGiven a set of pre-defined medical categories used in Evidence Based Medicine, we aim to automatically annotate sentences in medical abstracts with these labels.MethodWe constructed a corpus of 1,000 medical abstracts annotated by hand with specified medical categories (e.g. Intervention, Outcome). We explored the use of various features based on lexical, semantic, structural, and sequential information in the data, using Conditional Random Fields (CRF) for classification.ResultsFor the classification tasks over all labels, our systems achieved micro-averaged f-scores of 80.9% and 66.9% over datasets of structured and unstructured abstracts respectively, using sequential features. In labeling only the key sentences, our systems produced f-scores of 89.3% and 74.0% over structured and unstructured abstracts respectively, using the same sequential features. The results over an external dataset were lower (f-scores of 63.1% for all labels, and 83.8% for key sentences).ConclusionsOf the features we used, the best for classifying any given sentence in an abstract were based on unigrams, section headings, and sequential information from preceding sentences. These features resulted in improved performance over a simple bag-of-words approach, and outperformed feature sets used in previous work.