Prognostic Factors and Survival Rates in Early-stage Cervical Cancer Patients Treated with Radical Hysterectomy and Pelvic Lymphadenectomy

Objectives: To evaluate prognostic factors and survival rates in early-stage cervical cancer patients who had been treated with radical hysterectomy and pelvic lymphadenectomy (RHPL).Materials and Methods: Medical records and pathologic findings of 177 cervical cancer patients who had International Federation of Gynecology and Obstetrics (FIGO) stage IA2-IIA and underwent RHPL at Buddhachinaraj Phitsanulok Hospital from January 2005 to December 2016 were retrospectively reviewed. Clinicopathologic variables and treatment data were collected.Results: Among 177 patients, mean age was 49.9 ± 11.0 years. The median follow-up time was 42 months. Twenty-five patients had a recurrence and 7 patients died from disease. A five-year disease free survival (DFS) rate and a 5-year cancer-specific survival (CSS) rate were 89% and 96.6%, respectively. The independent prognostic factors for DFS were increasing age and pelvic lymph node metastasis (hazard ratio [HR] 1.06; 95%CI 1.02-1.10, and HR 4.63; 95%CI 1.21-17.64, respectively). No significant differences in FIGO stage, histology, positive surgical margin, parametrial involvement, pelvic lymph node metastasis, deep stromal invasion, lymph vascular space invasion, and tumor size were identified as independent prognostic factors for CSS. However, adenocarcinoma (AC) patients with parametrial involvement, pelvic lymph node metastasis, and postoperative treatment followed by concurrent chemoradiotherapy (CCRT) had a significantly worse survival outcome than those with squamous cell carcinoma (SCC) (HR 11.87; 95%CI 1.46-46.20, HR 7.00; 95%CI 1.55-31.66, and HR 7.20; 95%CI 1.57-32.85, respectively). Conclusion: Early-stage cervical cancer patients who underwent RHPL showed good survival rates. The independent prognostic factors for DFS were increasing age and pelvic lymph node metastasis whereas no prognostic factors for CSS were found. Furthermore, parametrial involvement, pelvic lymph node metastasis, and postoperative treatment followed by CCRT were likely to be predictors for poorer survival outcomes in AC than those in SCC

Potential of NIR spectroscopy to predict amygdalin content established by HPLC in intact almonds and classification based on almond bitterness

[EN] In this study, 360 intact almonds, half sweet and half bitter, were assessed by near-infrared (NIR) spectroscopy to predict amygdalin content (established by high performance liquid chromatography (HPLC)) and by applying partial least squares (PLS) to the spectral data. After optimising amygdalin extraction and chromatographic conditions, the amygdalin contents found by HPLC were not detected or below to 350 mg kg-1 for sweet almonds, and between 14,700 and 50,400mg kg 1 for bitter almonds. The intact almond spectra resulted in good predictions of amygdalin content with R2p of 0.939 and RMSEP of 0.373. Almonds were correctly classified into sweet and bitter by linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and PLS-DA, with sensitivity and specificity values higher than 0.94 for evaluation set samples. Based on these results, it can be concluded that NIR spectroscopy is a good non-destructive alternative to be used as an automatic in-line classification system by food industry.Victoria Cortes Lopez thanks the Spanish Ministry of Education, Culture and Sports for the FPU grant (FPU13/04202). The authors wish to thank the cooperative Agricoop for kindly donating the almonds.Cortes-Lopez, V.; Talens Oliag, P.; Barat Baviera, JM.; Lerma-García, MJ. (2018). Potential of NIR spectroscopy to predict amygdalin content established by HPLC in intact almonds and classification based on almond bitterness. Food Control. 91:68-75. https://doi.org/10.1016/j.foodcont.2018.03.040S68759

A hybrid gene selection algorithm based on interaction information for microarray-based cancer classification.

We address gene selection and machine learning methods for cancer classification using microarray gene expression data. Due to the high dimensionality of microarray data, traditional gene selection algorithms are filter-based, focusing on intrinsic properties of the data such as distance, dependency, and correlation. These methods are fast but select far too many genes to use for the classification task. In this work, we present a new hybrid filter-wrapper gene subset selection algorithm that is an improved modification of our prior algorithm. Our proposed method employs interaction information to rank candidate genes to add into a gene subset. It then conditionally adds one gene at a time into the current subset and verifies whether the resultant subset improves the classification performance significantly. Only significant genes are selected, and the candidate gene list is updated every time a gene is added to the subset. Thus, our gene selection algorithm is very dynamic. Experimental results on ten public cancer microarray data sets show that our method consistently outperforms prior gene selection algorithms in terms of classification accuracy, while requiring a small number of selected genes

Hyperspectral feature selection and fusion for detection of chicken skin tumors

We consider a feature selection method to detect skin tumors on chicken carcasses using hyperspectral reflectance data. This allows for faster data collection than does fluorescence data. A chicken skin tumor is an ulcerous lesion region surrounded by a region of thickened-skin. Detection of chicken tumors is a difficult detection problem because the tumors vary in size and shape; some tumors appear on the side of the chicken. In addition, different areas of normal chicken skin have a variety of hyperspectral response variations, some of which are very similar to the spectral responses of tumors. Similarly, different tumors and different parts of a tumor have different spectral responses. Thus, proper classifier training is needed and many false alarms are expected. Since the spectral responses of the lesion and the thickened-skin regions of tumors are considerably different, we train our feature selection algorithm to detect lesion regions and to detect thickened-skin regions separately; we then process the resultant images and we fuse the two HS detection results to reduce false alarms. Our new forward selection and modified branch and bound algorithm is used to select a small number of λ spectral features that are useful for discrimination. Initial results show that our method offers promise for a good tumor detection rate and a low false alarm rate

Hyperspectral Ratio Feature Selection: Agricultural Product Inspection Example

We describe a fast method for dimensionality reduction and feature selection of ratio features for classification in hyperspectral data. The case study chosen is to discriminate internally damaged almond nuts from normal ones. For this case study, we find that using the ratios of the responses in several wavebands provides better features than a subset of waveband responses. We find that use of the Euclidean Minimum Distance metric gives slightly better results than the more conventional Spectral Angle Mapper distance metric in a nearest neighbor classifier