Worsening of renal function (WRF) in acute heart failure (AHF) strongly predicts adverse clinical outcome. Plasma neutrophil gelatinase-associated lipocalin (NGAL) has been proposed as an earlier ...biomarker of tubular damage, but important methodological issues remain unsolved, particularly in AHF.
In 30 consecutive patients admitted for AHF, 108 serum NGAL (Alere system) measurements were performed at entry and in the first days of recovery, and reproducibility within the same blood samples was very high (r = 0.98). NGAL at entry was related to kidney function r = 0.51 vs. creatinine (Cr) and r = -0.49 vs. estimated glomerular filtration rate (eGFR), both P < 0.001, and weakly with hemoglobin (r = -0.36, P < 0.05) and C-reactive protein (CRP) (r = 0.26, P < 0.05). During hospitalization, WRF occurred in 26.7% of the patients. Baseline NGAL was only slightly higher in patients who developed WRF as compared to those who did not (151 ± 90 vs. 119 ± 75 ng/ml, NS), but it increased significantly in the following days, always preceding WRF occurrence (max. previous 24 h, average 95%, range 25-200%). The area under the Receiver Operating Characteristic (ROC) curve (AUC-ROC) was 0.69 for pathological NGAL at entry and 0.91 for delta NGAL changes during the first days.
In patients with AHF, serum NGAL measurement is highly reproducible and at entry it is related to baseline Cr and eGFR, but does not predict WRF during subsequent hospitalization. On the contrary, serial measurements of NGAL in the first days of hospitalization can accurately predict WRF.
Multiparametric (mp) magnetic resonance imaging (MRI) represents a robust tool for detecting prostate cancers (PCa). However, its interpretation requires skilled and specialized staff, and large ...investments of resources and time. To deal with this problem different artificial intelligence algorithms, based on Machine Learning (ML) and Deep Learning (DL), have been proposed and have been demonstrated useful to detect and characterize PCa. In this paper, we present a fully automated computer-aided diagnosis (CAD) system that utilizes either ML or DL techniques to segment PCa and we compared the results in terms of number of False Negative (FN) and False Positives (FPs) findings and accuracy of the segmentation masks. We present a DL model with two different input configurations: 2-channel and 3-channel. According to our results, DL techniques greatly decrease the volume of FPs and the number of FN compared to ML techniques, especially using the 3-channel model. Indeed, on the validation set, the number of FNs obtained by the DL model is lower than that by ML (respectively 7 and 11), while the median volume of FPs voxels decreased from 1077 \text{IQR}=362- 3787 to 518 \text{IQR}=170-1049 . The results obtained from this system could have a fairly obvious improvement by increasing the validation set, however preliminary results are encouraging and could be a strong contribution for personalized medicine.
Changing lifestyles and monitoring risk factors are two of the goals of secondary prevention programmes. To do this, it is necessary to investigate the level of patient's awareness regarding such ...factors. The purpose of this study was to investigate which factors the patients attribute as the cause of their ischemic disease and the level of their awareness.
One hundred and fifty-one patients from the Cardiology Rehabilitation Ward, who had undergone coronary bypass surgery, were enlisted (average age: 63.25 +/- 9.01 years; 85.4% men). During the psychological clinical interview, they were asked about the cause of their own coronary disease; their answers were then compared to the risk factors really present, to evaluate the index of awareness.
The factors mainly indicated as linked to coronopathy are stress, smoking habits, dyslipidemia, and unbalanced eating habits ( 32, 30, 23, and 21%, respectively). The spontaneous indication of each of these factors was present only in individuals with the mentioned risk factors, and it varied on the basis of some sociological variables (chi, P < 0.05).
Interventions of cardiovascular education must consider the personal data of the recipient individuals to increase the efficacy through the selection of targeted strategies.
Liver metastases (mts) from colorectal cancer (CRC) can have different responses to chemotherapy in the same patient. The aim of this study is to develop and validate a machine learning algorithm to ...predict response of individual liver mts. 22 radiomic features (RF) were computed on pretreatment portal CT scans following a manual segmentation of mts. RFs were extracted from 7x7 Region of Interests (ROIs) that moved across the image by step of 2 pixels. Liver mts were classified as non-responder (R-) if their largest diameter increased more than 3 mm after 3 months of treatment and responder (R+), otherwise. Features selection (FS) was performed by a genetic algorithm and classification by a Support Vector Machine (SVM) classifier. Sensitivity, specificity, negative (NPV) and positive (PPV) predictive values were evaluated for all lesions in the training and validation sets, separately. On the training set, we obtained sensitivity of 86%, specificity of 67%, PPV of 89% and NPV of 61%, while, on the validation set, we reached a sensitivity of 73%, specificity of 47%, PPV of 64% and NPV of 57%. Specificity was biased by the low number of R- lesions on the validation set. The promising results obtained in the validation dataset should be extended to a larger cohort of patient to further validate our method.Clinical Relevance- to personalize treatment of patients with metastastic colorectal cancer, based on the likelihood of response to chemotherapy of each liver metastasis.
Automatic segmentation of the prostate on Magnetic Resonance Imaging (MRI) is one of the topics on which research has focused in recent years as it is a fundamental first step in the building process ...of a Computer aided diagnosis (CAD) system for cancer detection. Unfortunately, MRI acquired in different centers with different scanners leads to images with different characteristics. In this work, we propose an automatic algorithm for prostate segmentation, based on a U-Net applying transfer learning method in a bi-center setting. First, T2w images with and without endorectal coil from 80 patients acquired at Center A were used as training set and internal validation set. Then, T2w images without endorectal coil from 20 patients acquired at Center B were used as external validation. The reference standard for this study was manual segmentation of the prostate gland performed by an expert operator. The results showed a Dice similarity coefficient >85% in both internal and external validation datasets.Clinical Relevance- This segmentation algorithm could be integrated into a CAD system to optimize computational effort in prostate cancer detection.
In the last decades, MRI was proven a useful tool for the diagnosis and characterization of Prostate Cancer (PCa). In the literature, many studies focused on characterizing PCa aggressiveness, but a ...few have distinguished between low-aggressive (Gleason Grade Group (GG) <=2) and high-aggressive (GG>=3) PCas based on biparametric MRI (bpMRI). In this study, 108 PCas were collected from two different centers and were divided into training, testing, and validation set. From Apparent Diffusion Coefficient (ADC) map and T2-Weighted Image (T2WI), we extracted texture features, both 3D and 2D, and we implemented three different methods of Feature Selection (FS): Minimum Redundance Maximum Relevance (MRMR), Affinity Propagation (AP), and Genetic Algorithm (GA). From the resulting subsets of predictors, we trained Support Vector Machine (SVM), Decision Tree, and Ensemble Learning classifiers on the training set, and we evaluated their prediction ability on the testing set. Then, for each FS method, we chose the best classifier, based on both training and testing performances, and we further assessed their generalization capability on the validation set. Between the three best models, a Decision Tree was trained using only two features extracted from the ADC map and selected by MRMR, achieving, on the validation set, an Area Under the ROC (AUC) equal to 81%, with sensitivity and specificity of 77% and 93%, respectively.Clinical Relevance- Our best model demonstrated to be able to distinguish low-aggressive from high-aggressive PCas with high accuracy. Potentially, this approach could help clinician to noninvasively distinguish between PCas that might need active treatment and those that could potentially benefit from active surveillance, avoiding biopsy-related complications.
Colorectal cancer (CRC) has the second-highest tumor incidence and is a leading cause of death by cancer. Nearly 20% of patients with CRC will have metastases (mts) at the time of diagnosis, and more ...than 50% of patients with CRC develop metastases during their disease. Unfortunately, only 45% of patients after a chemotherapy will respond to treatment. The aim of this study is to develop and validate a machine learning algorithm to predict response of individual liver mts, using CT scans. Understanding which mts will respond or not will help clinicians in providing a more efficient per-lesion treatment based on patient specific response and not only following a standard treatment. A group of 92 patients was enrolled from two Italian institutions. CT scans were collected, and the portal venous phase was manually segmented by an expert radiologist. Then, 75 radiomics features were extracted both from 7x7 ROIs that moved across the image and from the whole 3D mts. Feature selection was performed using a genetic algorithm. Results are presented as a comparison of the two different approaches of features extraction and different classification algorithms. Accuracy (ACC), sensitivity (SE), specificity (SP), negative and positive predictive values (NPV and PPV) were evaluated for all lesions (per-lesion analysis) and patients (per-patient analysis) in the construction and validation sets. Best results were obtained in the per-lesion analysis from the 3D approach using a Support Vector Machine as classifier. We reached on the training set an ACC of 81%, while on test set, we obtained SE of 76%, SP of 67%, PPV of 69% and NPV of 75%. On the validation set a SE of 61%, SP of 60%, PPV of 57% and NPV of 64% were reached. The promising results obtained in the validation dataset should be extended to a larger cohort of patient to further validate our method.Clinical Relevance- to develop a radiomics signatures predicting single liver mts response to therapy. A personalized mts approach is important to avoid unnecessary toxicity offering more suitable treatments and a better quality of life to oncological patients.
The aim of the study is to present a new Convolutional Neural Network (CNN) based system for the automatic segmentation of the colorectal cancer. The algorithm implemented consists of several steps: ...a pre-processing to normalize and highlights the tumoral area, the classification based on CNNs, and a post-processing aimed at reducing false positive elements. The classification is performed using three CNNs: each of them classifies the same regions of interest acquired from three different MR sequences. The final segmentation mask is obtained by a majority voting. Performances were evaluated using a semi-automatic segmentation revised by an experienced radiologist as reference standard. The system obtained Dice Similarity Coefficient (DSC) of 0.60, Precision (Pr) of 0.76 and Recall (Re) of 0.55 on the testing set. After applying the leave-one-out validation, we obtained a median DSC=0.58, Pr=0.74, Re=0.54. The promising results obtained by this system, if validated on a larger dataset, could strongly improve personalized medicine.
The aim of the study is to present and tune a fully automatic deep learning algorithm to segment colorectal cancers (CRC) on MR images, based on a U-Net structure. It is a multicenter study, ...including 3 different Italian institutions, that used 4 different MRI scanners. Two of them were used for training and tuning the systems, while the other two for the validation. The implemented algorithm consists of a pre-processing step to normalize and to highlight the tumoral area, followed by the CRC segmentation using different U-net structures. Automatic masks were compared with manual segmentations performed by three experienced radiologists, one at each center. The two best performing systems (called mdl2 and mdl3), obtained a median Dice Similarity Coefficient of 0.68(mdl2) - 0.69(mdl3), precision of 0.75(md/2) - 0.71(md/3), and recall of 0.69(mdl2) - 0.73(mdl3) on the validation set. Both systems reached high detection rates, 0.98 and 0.95, respectively, on the validation set. These encouraging results, if confirmed on larger dataset, might improve the management of patients with CRC, since it can be used as a fast and precise tool for further radiomics analyses. Clinical Relevance - To provide a reliable tool able to automatically segment CRC tumors that can be used as first step in future radiomics studies aimed at predicting response to chemotherapy and personalizing treatment.
