Recurrence After Operative Management of Intrahepatic Cholangiocarcinoma

INTRODUCTION: Data on recurrence after operation for intrahepatic cholangiocarcinoma (ICC) are limited. We sought to investigate rates and patterns of recurrence in patients after operative intervention for ICC. METHODS: We identified 301 patients who underwent operation for ICC between 1990 and 2011 from an international, multi-institutional database. Clinicopathologic data, recurrence patterns, and recurrence-free survival (RFS) were analyzed. RESULTS: During the median follow up duration of 31 months (range 1-208), 53.5% developed a recurrence. Median RFS was 20.2 months and 5-year actuarial disease-free survival, 32.1%. The most common site for initial recurrence after operation of ICC was intrahepatic (n = 98; 60.9%), followed by simultaneous intra- and extrahepatic disease (n = 30; 18.6%); 33 (21.0%) patients developed extrahepatic recurrence only as the first site of recurrence. Macrovascular invasion (hazard ratio [HR], 2.08; 95% confidence interval [CI], 1.34-3.21; P < .001), nodal metastasis (HR, 1.55; 95% CI, 1.01-2.45; P = .04), unknown nodal status (HR, 1.57; 95% CI, 1.10-2.25; P = .04), and tumor size ≥5 cm (HR, 1.84; 95% CI, 1.28-2.65; P < .001) were independently associated with increased risk of recurrence. Patients were assigned a clinical score from 0 to 3 according to the presence of these risk factors. The 5-year RFS for patients with scores of 0, 1, 2, and 3 was 61.8%, 36.2%, 19.5%, and 9.6%, respectively. CONCLUSION: Recurrence after operative intervention for ICC was common. Disease recurred both at intra- and extrahepatic sites with roughly the same frequency. Factors such as lymph node metastasis, tumor size, and vascular invasion predict highest risk of recurrence.

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.