Neuroendocrine Tumours of the Female Genital Tract: a Case-Based Imaging Review with Pathological Correlation

BACKGROUND: Both primary and secondary gynaecological neuroendocrine (NE) tumours are uncommon, and the literature is scarce concerning their imaging features. METHODS: This article reviews the epidemiological, clinical and imaging features with pathological correlation of gynaecological NE tumours. RESULTS: The clinical features of gynaecological NE tumours are non-specific and depend on the organ of origin and on the extension and aggressiveness of the disease. The imaging approach to these tumours is similar to that for other histological types and the Revised International Federation of Gynecology and Obstetrics (FIGO) Staging System also applies to NE tumours. Neuroendocrine tumours were recently divided into two groups: poorly differentiated neuroendocrine carcinomas (NECs) and well-differentiated neuroendocrine tumours (NETs). NECs include small cell carcinoma and large cell neuroendocrine carcinoma, while NETs account for typical and atypical carcinoids. Cervical small cell carcinoma and ovarian carcinoid are the most common gynaecological NE tumours. The former typically behaves aggressively; the latter usually behaves in a benign fashion and tends to be confined to the organ. CONCLUSION: While dealing with ovarian carcinoids, extra-ovarian extension, bilaterality and multinodularity raise the suspicion of metastatic disease. NE tumours of the endometrium and other gynaecological locations are very rare. TEACHING POINTS: • Primary or secondary neurondocrine (NE) tumours of the female genital tract are rare. • Cervical small cell carcinoma and ovarian carcinoids are the most common gynaecological NE tumours. • Cervical small cell carcinomas usually behave aggressively. • Ovarian carcinoids tend to behave in a benign fashion. • The imaging approach to gynaecological NE tumours and other histological types is similar.

Sepsis Mortality Prediction Based on Predisposition, Infection and Response

OBJECTIVE: To empirically test, based on a large multicenter, multinational database, whether a modified PIRO (predisposition, insult, response, and organ dysfunction) concept could be applied to predict mortality in patients with infection and sepsis. DESIGN: Substudy of a multicenter multinational cohort study (SAPS 3). PATIENTS: A total of 2,628 patients with signs of infection or sepsis who stayed in the ICU for >48 h. Three boxes of variables were defined, according to the PIRO concept. Box 1 (Predisposition) contained information about the patient's condition before ICU admission. Box 2 (Injury) contained information about the infection at ICU admission. Box 3 (Response) was defined as the response to the infection, expressed as a Sequential Organ Failure Assessment score after 48 h. INTERVENTIONS: None. MAIN MEASUREMENTS AND RESULTS: Most of the infections were community acquired (59.6%); 32.5% were hospital acquired. The median age of the patients was 65 (50-75) years, and 41.1% were female. About 22% (n=576) of the patients presented with infection only, 36.3% (n=953) with signs of sepsis, 23.6% (n=619) with severe sepsis, and 18.3% (n=480) with septic shock. Hospital mortality was 40.6% overall, greater in those with septic shock (52.5%) than in those with infection (34.7%). Several factors related to predisposition, infection and response were associated with hospital mortality. CONCLUSION: The proposed three-level system, by using objectively defined criteria for risk of mortality in sepsis, could be used by physicians to stratify patients at ICU admission or shortly thereafter, contributing to a better selection of management according to the risk of death.

Automatic Small Bowel Tumor Diagnosis by Using Multi-Scale Wavelet-Based Analysis in Wireless Capsule Endoscopy Images

BACKGROUND: Wireless capsule endoscopy has been introduced as an innovative, non-invasive diagnostic technique for evaluation of the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the output of this technique is an 8 hours video, whose analysis by the expert physician is very time consuming. Thus, a computer assisted diagnosis tool to help the physicians to evaluate CE exams faster and more accurately is an important technical challenge and an excellent economical opportunity. METHOD: The set of features proposed in this paper to code textural information is based on statistical modeling of second order textural measures extracted from co-occurrence matrices. To cope with both joint and marginal non-Gaussianity of second order textural measures, higher order moments are used. These statistical moments are taken from the two-dimensional color-scale feature space, where two different scales are considered. Second and higher order moments of textural measures are computed from the co-occurrence matrices computed from images synthesized by the inverse wavelet transform of the wavelet transform containing only the selected scales for the three color channels. The dimensionality of the data is reduced by using Principal Component Analysis. RESULTS: The proposed textural features are then used as the input of a classifier based on artificial neural networks. Classification performances of 93.1% specificity and 93.9% sensitivity are achieved on real data. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis systems to assist physicians in their clinical practice.