Introduction: After initial treatment, differentiated thyroid cancer (DTC) patients are stratified as low and high risk based on clinical/pathological features. Recently, a risk stratification based on additional clinical data accumulated during follow-up has been proposed. Objective: To evaluate the predictive value of delayed risk stratification (DRS) obtained at the time of the first diagnostic control (8-12 months after initial treatment). Methods: We reviewed 512 patients with DTC whose risk assessment was initially defined according to the American (ATA) and European Thyroid Association (ETA) guidelines. At the time of the first control, 8-12 months after initial treatment, patients were re-stratified according to their clinical status: DRS. Results: Using DRS, about 50% of ATA/ETA intermediate/high-risk patients moved to DRS low-risk category, while about 10% of ATA/ETA low-risk patients moved to DRS high-risk category. The ability of the DRS to predict the final outcome was superior to that of ATA and ETA. Positive and negative predictive values for both ATA (39.2 and 90.6% respectively) and ETA (38.4 and 91.3% respectively) were significantly lower than that observed with the DRS (72.8 and 96.3% respectively, P!0.05). The observed variance in predicting final outcome was 25.4% for ATA, 19.1% for ETA, and 62.1% for DRS. Conclusions: Delaying the risk stratification of DTC patients at a time when the response to surgery and radioiodine ablation is evident allows to better define individual risk and to better modulate the subsequent follow-up.

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Women with endometriosis at first pregnancy have an increased risk of impaired obstetric outcome, while a reduced number of complications occur in the successive gestation. Therefore, it is worthy for obstetricians to increase the surveillance in nulliparous women with endometriosis during pregnancy.

Objectives: To use a digital dermoscopy analyzer with a series of "borderline" pigmentary skin lesions (ie, clinically atypical nevi and early melanoma) to find correlation between the studied variables and to determine their discriminating power with respect to histological diagnosis.Design: A total of 147 pigmentary skin lesions were histologically examined by 3 experienced dermatopathologists and identified as nevi (n = 90) and melanomas (n = 57). The system evaluated 36 variables to be studied as possible discriminant variables, grouped into 4 categories: geometries, colors, textures, and islands of color.Setting: University medical department.Patients: A sample of patients with excised pigmen-tary skin lesions (nevi and melanomas).Main Outcome Measures: Sensitivity, specificity, and accuracy of the model for evaluating "borderline" pigmentary skin lesions.Results: After multivariate stepwise discriminant analysis, only 13 variables were selected to compute the canonical discriminant function. Conclusion:The present method made it possible to determine which objective variables are important for distinguishing atypical benign pigmentary skin lesions and early melanoma.

Noninvasive diagnostic methods such as dermoscopy or epiluminescence light microscopy have been developed in an attempt to improve diagnostic accuracy of pigmented skin lesions. The evaluation of the many morphologic characteristics of pigmented skin lesions observable by epiluminescence light microscopy, however, is often extremely complex and subjective. With the aim of obviating these problems of qualitative interpretation, methods based on mathematical analysis of pigmented skin lesions have recently been designed. These methods are based on computerized analysis of digital images obtained by epiluminescence light microscopy. In this study we used a digital dermoscopy analyzer with 147 clinically atypical pigmented skin lesions (90 nevi and 57 melanomas) to determine its discriminating power with respect to histologic diagnosis. The system evaluated 48 objective parameters used to train an artificial neural network. Using the artificial neural network with 10 variables selected by a stepwise procedure, we obtained a maximum accuracy in distinguishing melanoma from benign lesions of about 93%. Comparing this result with those of the many studies using classical epiluminescence light microscopy, it emerges that the method proposed is equal or even superior in diagnostic accuracy and has the advantage of not depending on the expertise of the clinician who examines the lesion.

High-grade gliomas (HGG) have a poor outcome, however, prognostic subgroups of patients may be individuated by some clinico-biological parameters. It was recently demonstrated that the main response of HGG to therapy is autophagic death. Autophagy is involved in tumor suppression, and is defective in HGG, in which we previously found an underexpression of beclin 1 autophagic gene protein product. Underexpression of Beclin 1 protein has been correlated to poor patient outcome in other tumor types. In this paper, the prognostic role of Beclin 1 expression in HGG patients was investigated. We first evaluated the tumor cell cytoplasmic expression of Beclin 1 protein (BPCE), in a sample of 76 HGG by immunohistochemistry, and compared it with cell proliferation and apoptosis. We found high BPCE score positively correlated with apoptosis, and negatively with cell proliferation (p < 0.05). We then correlated BPCE score with survival and other prognostic parameters (histological grading, MGMT gene methylation status, age, patient performance status according to the Karnofski classification (KPS), extent of surgery, radiation therapy (RT) modality, temozolomide chemotherapy (TMZ CHT), and optimal/suboptimal post-surgical treatment). Forty-seven (61.8%) and twenty-nine (38.2%) patients showed high and low BPCE scores, respectively. BPCE showed statistically significant correlations with survival both at the univariate (p = 0.03) and multivariate analysis (p = 0.037). High BPCE was also positively correlated with high KPS values (p = 0.023), and with the accomplishment of an optimal postoperative therapy (p = 0.037). Furthermore, among patients showing a MGMT methylated gene, survival was significantly higher in cases with a higher BPCE score. BPCE score might be added to pathological evaluation of HGG for prognostic purposes.

Since advanced melanoma remains practically incurable, early detection is an important step toward a reduction in mortality. High expectations are entertained for a technique known as dermoscopy or epiluminescence light microscopy; however, evaluation of pigmented skin lesions by this method is often extremely complex and subjective. To obviate the problem of qualitative interpretation, methods based on mathematical analysis of pigmented skin lesions, such as digital dermoscopy analysis, have been developed. In the present study, we used a digital dermoscopy analyzer (DBDermo-Mips system) to evaluate a series of 588 excised, clinically atypical, flat pigmented skin lesions (371 benign, 217 malignant). The analyzer evaluated 48 parameters grouped into 4 categories (geometries, colors, textures and islands of color), which were used to train an artificial neural network. To evaluate the diagnostic performance of the neural network and to check it during the training process, we used the error area over the receiver operating characteristic curve. The discriminating power of the digital dermoscopy analyzer plus artificial neural network was compared with histologic diagnosis. A feature selection procedure indicated that as few as 13 of the variables were sufficient to discriminate the 2 groups of lesions, and this also ensured high generalization power. The artificial neural network designed with these variables enabled a diagnostic accuracy of about 94%. In conclusion, the good diagnostic performance and high speed in reading and analyzing lesions (real time) of our method constitute an important step in the direction of automated diagnosis of pigmented skin lesions.

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