Bone mineral density (BMD) measurement by hip dual-energy X-ray absorptiometry (DXA) is considered the best predictor of osteoporotic fracture risk. BMD takes into account only in part the bone cross-sectional area that is an important determinant of both bone compression strength and of bending breaking resistance. From DXA measurements of proximal radius (Osteoplan, NIM, Verona, Italy) we obtained the projected outer diameter (D) and the mean diameter of the medulla (d), by an algorithm based on the assumption of a constant cortical volumetric density of 1050 g/cm3. The algorithm was validated by the good correlation found (r = 0.8) between calculated d and that actually measured by peripheral quantitative tomography (pQCT; XCT 960, Stratec, Unitrem, Italy) at the same radial site. The D and d values were used to calculate a bending breaking resistance index (BBRI) that is a component of the cross-sectional moment of inertia. The BBRI measured in 5460 women aged 35-89 years, was stable up to the age of 65-70 years and rapidly declined thereafter by 0.7% per year. This profile appears to be due to the fact that the increase in medullary area is compensated in terms of mechanical resistance by enlargement of cross-sectional area. In 68 women with either previous femoral neck (n = 41) or pertrochanteric fracture (n = 27) DXA measurements at proximal and ultradistal radius, lumbar spine and femoral neck were obtained together with the evaluation of proximal radius BBRI. The diagnostic accuracy of BBRI was somewhat comparable to that of spine and femoral neck BMD and significantly superior to that of ultradistal and proximal radius BMD, from which it was derived. Despite the obvious limitation of the cross-sectional nature of this study, our results indicate that a simple re-elaboration of the data obtained by peripheral radial densitometry may achieve diagnostic accuracy for hip fracture risk assessment only marginally lower than that of the direct measure of the BMD of the femoral neck. They also give additional support to the view that bone geometry, particularly for compact skeletal segments, is a determinant of its strength at least as important as bone density.

Preoperative autologous blood donation (PABD) is a well established transfusion practice in elective orthopaedic surgery, involving immunologic and infective advantages but also involving exposure to not negligible risks, and costs as well. The aim of this study was to assess the real need for blood transfusions in primary total knee arthroplasty (TKA). Between January 2000 and July 2005, 214 patients underwent primary unilateral TKA. Altogether, 416 autologous blood units were collected, however only 47 (11.3%) were transfused. Thirty-eight patients (17.8%) received autologous blood, while 4 of them (10.5%) also received allogeneic blood. Based on the results of this study, PABD should be recommended in well selected patients undergoing TKA: older female patients with a low basal haemoglobin level.

Based on the results of this study, PABD is not necessary in most patients undergoing TKA, although older female patients with low basal haemoglobin levels could benefit from a predeposit programme and/or erythropoietin support in order to reduce the risk of exposure to allogeneic blood.

Lung cancer is one of the deadliest diseases worldwide. Computed Tomography (CT) images are a powerful tool for investigating the structure and texture of lung nodules. For a long time, trained radiologists have performed the grading and staging of cancer severity by relying on radiographic images. Recently, radiomics has been changing the traditional workflow for lung cancer staging by providing the technical and methodological means to analytically quantify lesions so that more accurate predictions could be performed while reducing the time required from each specialist to perform such tasks. In this work, we implemented a pipeline for identifying a radiomic signature composed of a reduced number of features to discriminate between adenocarcinomas and other cancer types. In addition, we also investigated the reproducibility of this radiomic study analysing the performances of the classification models on external validation data. In detail, we first considered two publicly available datasets, namely D1 and D2, composed of n = 262 and n = 89 samples, respectively. Ten significant features, according to univariate AUC evaluated on D1, were retained. Mann–Whitney U tests recognised three of these features to have a statistically different distribution, with a p-value < 0.05. Then, we collected n = 51 CT images from patients with lung nodules at the Azienda Ospedaliero—Universitaria “Policlinico Riuniti” in Foggia. Resident radiologists manually annotated the lung lesions in images to allow the subsequent analysis of the malignancy regions. We designed a pipeline for feature extraction from the Volumes of Interest in order to generate a third dataset, i.e., D3. Several experiments have been performed showing that the selected radiomic signature not only allowed the discrimination of lung adenocarcinoma from other cancer types independently from the input dataset used for training the models, but also allowed reaching good classification performances also on external validation data; in fact, the radiomic signature computed on D1 and evaluated on the local cohort allowed reaching an AUC of 0.70 (p<0.001) for the task of predicting the histological subtype.

No abstract