Evaluation of efficacy and safety of new high-density dyes for chromovitrectomy

The purpose of this study is to evaluate the safety and efficacy of two novel heavy dyes for macular surgery: DoubledyneTM and TwinTM. One eye from each of 144 patients undergoing surgery for macular hole or macular pucker was included in the study. The eyes were randomly divided into two groups according to the dye used during surgery. Best correct visual acuity (BCVA), intraocular pressure (IOP) and retinal morphology assessed by ocular coherence tomography (OCT) were evaluated before and 1, 3, 6 and 12 months after surgery. Only one surgeon performed each operation and provided a score ranging from 1 (poor) to 10 (excellent) for quality of staining and comfort in surgery. Statistical analysis was carried out with SPSS to compare parameters before and after surgery and between the two groups. No statistical differences were recorded in quality of staining (p = 0.11), in surgery comfort (p = 0.17) and total time of surgery (p = 0.44) between the two groups. BCVA statistically improved and central macular thickness (CMT) statistically decreased after surgery in both groups (p < 0.05). No toxic dye-related complications or long-term ones affecting the retina were observed in either group. According to this data, although confirmation in further studies with larger populations and longer follow up is required, DoubledyneTM and TwinTM proved to be safe and effective dyes for macular surgery

Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions

The aim of the study was to estimate the diagnostic accuracy of textural, morpho- logical and dynamic features, extracted by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images, by carrying out univariate and multivariate statistical analyses including artificial intelligence approaches. Methods: In total, 85 patients with known breast lesion were enrolled in this retrospective study according to regulations issued by the local Institutional Review Board. All patients underwent DCE-MRI examination. The reference standard was pathology from a surgical specimen for malignant lesions and pathology from a surgical specimen or fine needle aspiration cytology, core or Tru-Cut needle biopsy for benign lesions. In total, 91 samples of 85 patients were ana- lyzed. Furthermore, 48 textural metrics, 15 morphological and 81 dynamic parameters were extracted by manually segmenting regions of interest. Statistical analyses including univariate and multivari- ate approaches were performed: non-parametric Wilcoxon–Mann–Whitney test; receiver operating characteristic (ROC), linear classifier (LDA), decision tree (DT), k-nearest neighbors (KNN), and support vector machine (SVM) were utilized. A balancing approach and feature selection methods were used. Results: The univariate analysis showed low accuracy and area under the curve (AUC) for all considered features. Instead, in the multivariate textural analysis, the best performance (accuracy (ACC) = 0.78; AUC = 0.78) was reached with all 48 metrics and an LDA trained with balanced data. The best performance (ACC = 0.75; AUC = 0.80) using morphological features was reached with an SVM trained with 10-fold cross-variation (CV) and balanced data (with adaptive synthetic (ADASYN) function) and a subset of five robust morphological features (circularity, rectangularity, sphericity, gleaning and surface). The best performance (ACC = 0.82; AUC = 0.83) using dynamic features was reached with a trained SVM and balanced data (with ADASYN function). Conclusion: Multivariate analyses using pattern recognition approaches, including all morphological, textural and dynamic features, optimized by adaptive synthetic sampling and feature selection operations obtained the best results and showed the best performance in the discrimination of benign and malignant lesions

Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography in the Breast Lesions Classification

The aim of the study was to estimate the diagnostic accuracy of textural features extracted by dual-energy contrast-enhanced mammography (CEM) images, by carrying out univariate and multivariate statistical analyses including artificial intelligence approaches. In total, 80 patients with known breast lesion were enrolled in this prospective study according to regulations issued by the local Institutional Review Board. All patients underwent dual-energy CEM examination in both craniocaudally (CC) and double acquisition of mediolateral oblique (MLO) projections (early and late). The reference standard was pathology from a surgical specimen for malignant lesions and pathology from a surgical specimen or fine needle aspiration cytology, core or Tru-Cut needle biopsy, and vacuum assisted breast biopsy for benign lesions. In total, 104 samples of 80 patients were analyzed. Furthermore, 48 textural parameters were extracted by manually segmenting regions of interest. Univariate and multivariate approaches were performed: non-parametric Wilcoxon–Mann–Whitney test; receiver operating characteristic (ROC), linear classifier (LDA), decision tree (DT), k-nearest neighbors (KNN), artificial neural network (NNET), and support vector machine (SVM) were utilized. A balancing approach and feature selection methods were used. The univariate analysis showed low accuracy and area under the curve (AUC) for all considered features. Instead, in the multivariate textural analysis, the best performance considering the CC view (accuracy (ACC) = 0.75; AUC = 0.82) was reached with a DT trained with leave-one-out cross-variation (LOOCV) and balanced data (with adaptive synthetic (ADASYN) function) and a subset of three robust textural features (MAD, VARIANCE, and LRLGE). The best performance (ACC = 0.77; AUC = 0.83) considering the early-MLO view was reached with a NNET trained with LOOCV and balanced data (with ADASYN function) and a subset of ten robust features (MEAN, MAD, RANGE, IQR, VARIANCE, CORRELATION, RLV, COARSNESS, BUSYNESS, and STRENGTH). The best performance (ACC = 0.73; AUC = 0.82) considering the late-MLO view was reached with a NNET trained with LOOCV and balanced data (with ADASYN function) and a subset of eleven robust features (MODE, MEDIAN, RANGE, RLN, LRLGE, RLV, LZLGE, GLV_GLSZM, ZSV, COARSNESS, and BUSYNESS). Multivariate analyses using pattern recognition approaches, considering 144 textural features extracted from all three mammographic projections (CC, early MLO, and late MLO), optimized by adaptive synthetic sampling and feature selection operations obtained the best results (ACC = 0.87; AUC = 0.90) and showed the best performance in the discrimination of benign and malignant lesions

Induction of VX2 para-renal carcinoma in rabbits: generation of animal model for loco-regional treatments of solid tumors

BACKGROUND: Animal models of para-renal cancer can provide useful information for the evaluation of tumor response to loco-regional therapy experiments in solid tumors. The aim of our study was to establish a rabbit para-renal cancer model using locally implanted VX2 tumors. METHODS: In order to generate a rabbit model of para-renal cancer, we established four hind limb donor rabbits by using frozen VX2 tumor samples. Following inoculation, rabbits were monitored for appetite and signs of pain. Viable tumors appeared as palpable nodules within 2 weeks of inoculation. Tumor growth was confirmed in all rabbits by high-resolution ultrasound analysis and histology. Once tumor growth was established, hind limb tumors extraction was used for tumor line propagation and para-renal tumor creation. Twenty-one rabbit models bearing para-renal cancer were established by implanting VX2 tumor into the para-renal capsula. Tumors developed into discreet 2-3 cm nodules within 1-3 weeks of implantation. Serial renal ultrasonography follow-up, starting 1 week after tumor implantation, was performed. Two weeks after tumor implantation, rabbits were euthanized and tumors and other organs were collected for histopathology. RESULTS: Tumor growth after VX2 tumor fragment implantation was confirmed in all rabbits by high-resolution ultrasound (US) imaging examinations of the para-renal regions and was measured with digital caliper. The para-renal injection of VX2 tumor fragments, achieved tumor growth in 100% of cases. All data were confirmed by histological analysis. CONCLUSIONS: We generated for the first time, a model of para-renal cancer by surgical tumor implantation of VX2 frozen tumor fragments into rabbit's para-renal region. This method minimizes the development of metastases and the use of non-necrotic tumors and will optimize the evaluation of tumor response to loco-regional therapy experiments.Background: Animal models of para-renal cancer can provide useful information for the evaluation of tumor response to loco-regional therapy experiments in solid tumors. The aim of our study was to establish a rabbit para-renal cancer model using locally implanted VX2 tumors. Methods: In order to generate a rabbit model of para-renal cancer, we established four hind limb donor rabbits by using frozen VX2 tumor samples. Following inoculation, rabbits were monitored for appetite and signs of pain. Viable tumors appeared as palpable nodules within 2 weeks of inoculation. Tumor growth was confirmed in all rabbits by high-resolution ultrasound analysis and histology. Once tumor growth was established, hind limb tumors extraction was used for tumor line propagation and para-renal tumor creation. Twenty-one rabbit models bearing para-renal cancer were established by implanting VX2 tumor into the para-renal capsula. Tumors developed into discreet 2-3 cm nodules within 1-3 weeks of implantation. Serial renal ultrasonography follow-up, starting 1 week after tumor implantation, was performed. Two weeks after tumor implantation, rabbits were euthanized and tumors and other organs were collected for histopathology. Results: Tumor growth after VX2 tumor fragment implantation was confirmed in all rabbits by high-resolution ultrasound (US) imaging examinations of the para-renal regions and was measured with digital caliper. The para-renal injection of VX2 tumor fragments, achieved tumor growth in 100% of cases. All data were confirmed by histological analysis. Conclusions: We generated for the first time, a model of para-renal cancer by surgical tumor implantation of VX2 frozen tumor fragments into rabbit's para-renal region. This method minimizes the development of metastases and the use of non-necrotic tumors and will optimize the evaluation of tumor response to loco-regional therapy experiments

Radiomic and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to Detect Breast Malignant Lesions

Purpose:The purpose of this study was to discriminate between benign and malignant breast lesions through several classifiers using, as predictors, radiomic metrics extracted from CEM and DCE-MRI images. In order to optimize the analysis, balancing and feature selection procedures were performed. Methods: Fifty-four patients with 79 histo-pathologically proven breast lesions (48 malignant lesions and 31 benign lesions) underwent both CEM and DCE-MRI. The lesions were retrospectively analyzed with radiomic and artificial intelligence approaches. Forty-eight textural metrics were extracted, and univariate and multivariate analyses were performed: non-parametric statistical test, receiver operating characteristic (ROC) and machine learning classifiers. Results: Considering the single metrics extracted from CEM, the best predictors were KURTOSIS (area under ROC curve (AUC) = 0.71) and SKEWNESS (AUC = 0.71) calculated on late MLO view. Considering the features calculated from DCE-MRI, the best predictors were RANGE (AUC = 0.72), ENERGY (AUC = 0.72), ENTROPY (AUC = 0.70) and GLN (gray-level nonuniformity) of the gray-level run-length matrix (AUC = 0.72). Considering the analysis with classifiers and an unbalanced dataset, no significant results were obtained. After the balancing and feature selection procedures, higher values of accuracy, specificity and AUC were reached. The best performance was obtained considering 18 robust features among all metrics derived from CEM and DCE-MRI, using a linear discriminant analysis (accuracy of 0.84 and AUC = 0.88). Conclusions: Classifiers, adjusted with adaptive synthetic sampling and feature selection, allowed for increased diagnostic performance of CEM and DCE-MRI in the differentiation between benign and malignant lesions