Deep-Learning Based Quantification of Bovine Oocyte Quality From Microscopy Images.

The success rate of bovine in vitro embryo reproduction is low and highly dependent on the oocyte quality. The selection of the oocyte to be fertilized is done by the embryologists' visual examination of oocytes. It is time-consuming, subjective, and inconsistent between specialists in the area. In this paper, a semi-automatic solution is proposed to score the quality of an immature oocyte. It consists of a deep learning model to classify oocyte competence. The model was trained and tested with real data, composed of images of immature oocytes and their label of whether they developed into blastocysts after fertilization. To the best of our knowledge, automated bovine oocyte classification was not attempted before, but experimental results show that our proposed solution is more robust and objective than specialists' visual assessment and comparable with other works on human oocytes.Clinical relevance- This establishes a semi-automatic real-time method to score bovine immature oocytes, based on stereo-microscopy images. Our method will significantly reduce the time of in vitro embryo production and its success.

Inflammatory Microenvironment in Early Non-Small Cell Lung Cancer: Exploring the Predictive Value of Radiomics.

Patient prognosis is a critical consideration in the treatment decision-making process. Conventionally, patient outcome is related to tumor characteristics, the cancer spread, and the patients' conditions. However, unexplained differences in survival time are often observed, even among patients with similar clinical and molecular tumor traits. This study investigated how inflammatory radiomic features can correlate with evidence-based biological analyses to provide translated value in assessing clinical outcomes in patients with NSCLC. We analyzed a group of 15 patients with stage I NSCLC who showed extremely different OS outcomes despite apparently harboring the same tumor characteristics. We thus analyzed the inflammatory levels in their tumor microenvironment (TME) either biologically or radiologically, focusing our attention on the NLRP3 cancer-dependent inflammasome pathway. We determined an NLRP3-dependent peritumoral inflammatory status correlated with the outcome of NSCLC patients, with markedly increased OS in those patients with a low rate of NLRP3 activation. We consistently extracted specific radiomic signatures that perfectly discriminated patients' inflammatory levels and, therefore, their clinical outcomes. We developed and validated a radiomic model unleashing quantitative inflammatory features from CT images with an excellent performance to predict the evolution pattern of NSCLC tumors for a personalized and accelerated patient management in a non-invasive way.

Fetal Heart Rate Fragmentation.

Intrapartum fetal monitoring's primary goal is to avoid adverse perinatal outcomes related to hypoxia/acidosis without increasing unnecessary interventions. Recently, a set of indices were proposed as new biomarkers to analyze heart rate (HR), termed HR fragmentation (HRF). In this work, the HRF indices were applied to intrapartum fetal heart rate (FHR) traces to evaluate fetal acidemia. The fragmentation method produces four indices: PIP-Percentage of inflection points; IALS-Inverse of the average length of acceleration/deceleration segments; PSS-Percentage of short segments; PAS-Percentage of alternating segments. On the other hand, the symbolic approach studied the existence of different patterns of length four. We applied the measures to 246 selected FHR recordings sampled at 4 and 2 Hz, where 39 presented umbilical artery's pH ≤ 7.15. When applied to the 4 Hz FHR, the PIP, IASL, and PSS showed significantly higher values in the traces from acidemic fetuses. In comparison, the percentage of "words" W 1 h and W 2 s showed lower values for those traces. Furthermore, when using the 2 Hz, only IASL, W 0, and W 2 m achieved significant differences between traces from both acidemic and normal fetuses. Notwithstanding, the ideal sampling frequency is yet to be established. The fragmentation indices correlated with Sisporto variability measures, especially short-term variability. Accordingly, the fragmentation indices seem to be able to detect pathological patterns in FHR tracings. These indices have the advantage of being suitable and straightforward to apply in real-time analysis. Future studies should combine these indexes with others used successfully to detect fetal hypoxia, improving the power of discrimination in a larger dataset.

Preliminary Report on Computed Tomography Radiomics Features as Biomarkers to Immunotherapy Selection in Lung Adenocarcinoma Patients.

PURPOSE: To assess the efficacy of radiomics features obtained by computed tomography (CT) examination as biomarkers in order to select patients with lung adenocarcinoma who would benefit from immunotherapy. METHODS: Seventy-four patients (median age 63 years, range 42-86 years) with histologically confirmed lung cancer who underwent immunotherapy as first- or second-line therapy and who had baseline CT studies were enrolled in this approved retrospective study. As a control group, we selected 50 patients (median age 66 years, range 36-86 years) from 2005 to 2013 with histologically confirmed lung adenocarcinoma who underwent chemotherapy alone or in combination with targeted therapy. A total of 573 radiomic metrics were extracted: 14 features based on Hounsfield unit values specific for lung CT images; 66 first-order profile features based on intensity values; 43 second-order profile features based on lesion shape; 393 third-order profile features; and 57 features with higher-order profiles. Univariate and multivariate statistical analysis with pattern recognition approaches and the least absolute shrinkage and selection operator (LASSO) method were used to assess the capability of extracted radiomics features to predict overall survival (OS) and progression free survival (PFS) time. RESULTS: A total of 38 patients (median age 61; range 41-78 years) with confirmed lung adenocarcinoma and subjected to immunotherapy satisfied inclusion criteria, and 50 patients in a control group were included in the analysis The shift in the center of mass of the lesion due to image intensity was significant both to predict OS in patients subjected to immunotherapy and to predict PFS in patients subjected to immunotherapy and in patients in the control group. With univariate analysis, low diagnostic accuracy was reached to stratify patients based on OS and PFS time. Regarding multivariate analysis, considering the robust (two morphological features, three textural features and three higher-order statistical metrics) application of the LASSO approach and all patients, a support vector machine reached the best results for stratifying patients based on OS (area under curve (AUC) of 0.89 and accuracy of 81.6%). Alternatively, considering the robust predictors (six textural features and one higher-order statistical metric) and application of the LASSO approach including all patients, a decision tree reached the best results for stratifying patients based on PFS time (AUC of 0.96 and accuracy of 94.7%). CONCLUSIONS: Specific radiomic features could be used to select patients with lung adenocarcinoma who would benefit from immunotherapy because a subset of imaging radiomic features useful to predict OS or PFS time were different between the control group and the immunotherapy group.

The texture analysis as a predictive method in the assessment of the cytological specimen of CT-guided FNAC of the lung cancer.

The lung cancer is the principle cause of the worldwide deaths and its prognosis is poor with a 5-year overall survival rate. Computed tomography (CT) gives many information about the prognosis, but the problem is the subject interpretation of the findings. Thanks to the computer-aided diagnosis/detection (CAD), it is possible to reduce the second opinion. "Radiomics" is an extension of CAD and overlaps the quantitative imaging data of the CT texture analysis (CTTA) with the clinical information, increasing the power and precision of the decision going through the personalized medicine. The aim of this study is to describe the role of the radiomics in the characterization of the pulmonary nodule. For this study, we retrospectively analyzed the images of the 87 NSCLC patients with a waiver of informed consent from the Institutional Review Board (IRB) at the Campania University "Luigi Vanvitelli" of Naples. All tumors were semiautomatically segmented by a radiologist with 10 years of experience using three diameters (AW Server 3.2). The examinations were acquired using 128 MDCT (GSI CT, GE) with a peak tube voltage of 120 kVp, tube current of 100 or 200 mA, and rotation times of 0.5 or 0.8 s. To confirm the imaging results, the FNAC was performed and for every nodule the following parameters were extracted: the presence of the solid component (named = 1), papillary component (named = 2), and mixed component (named = 3). Feature calculation was performed using the HealthMyne software and Integrated Platform That Enables Better Patient Management Decisions For Oncology. The radiologist uses the Rapid Precise Metrics (RPM)™ functionality to identify a lesion with the algorithm and these methods are put to work. The correlation between each feature and the tumor volume was calculated using a two-step cluster statistical analysis. In this retrospective study, in one year from 2018 to 2019 20 patients with lung adenocarcinoma confirmed with FNAC were enrolled. The pathologic results were subdivided into three categories: the solid architecture (group 1), papillary architecture (group 2), and mixed architecture (group 3). Nine lesions resulted with component 1, seven patients with component 2, and 3 patients with component 3. Eight females and 12 males with a median age 61 and 15 years (mean ± SD = 67.4 ± 9.7 years, range 39-73 years) were enrolled. The two results suggest, with p < 0.05, that the GGO variable is a good discriminating estimator of the kurtosis variable: GGO = "no" implies a high kurtosis value, while GGO = "yes" implies a low value. The numerous data obtained from the automatic analysis allow to have a fertile ground on which to develop a new concept of medicine which is precision medicine. The limit of this study is the poor sample. In the future, in order to have a more mature and consolidated discipline, it is necessary to increase the large scale of observations with further studies to establish the rigorous evaluation criteria. In order for radiomics to mature as a discipline in the future, it will be necessary to develop studies that consolidate its role to standardize the collected data.

Evaluation of Dose Homogeneity in Cone-Beam Breast Computed Tomography.

The aim of this study is to determine, via measurements on phantoms and Monte Carlo (MC) simulations, the dose distribution of absorbed dose in a cone-beam breast computed tomography scan. The absorbed dose volume distribution was measured inside a polyethylene cylindrical phantom, simulating adipose breast tissue, using LiF:Mg,Ti thermoluminescence dosimeters. A reasonable agreement (between 2 and 8%) between the simulated and measured data was observed. The 3D distribution of absorbed dose was evaluated at 40, 60 and 80 kV in a phantom simulating a pendant breast. MC simulations indicate a significantly lower spread of volume dose than in mammography. The dose variation along the radial distance in the simulated phantoms was in the range of 4‒14%. These findings might be useful when devising models for breast imaging dose assessment that take into account the uneven distribution of the glandular mass in the breast volume.