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Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) is a promising biodegradable bio-based material, which is designed for a vast range of applications, depending on its composite. This study aims to assess the degradability of a PHBV-based compound under different conditions. The research group followed different methodological approaches and assessed visual and mass changes, mechanical and morphological properties, spectroscopic and structural characterisation, along with thermal behaviour. The Ph-Stat (enzymatic degradation) test and total dry solids (TDS)/total volatile solids (TVS) measurements were carried out. Finally, the team experimentally evaluated the amount of methane and carbon dioxide produced, i.e., the degree of biodegradation under aerobic conditions. According to the results, different types of tests have shown differing effects of environmental conditions on material degradation. In conclusion, this paper provides a summary of the investigations regarding the degradation behaviour of the PHBV-based compound under varying environmental factors. The main strengths of the study lie in its multi-faceted approach, combining assessments of PHBV-based compound degradability under different conditions using various analytical tools, such as visual and mass changes, mechanical and morphological properties, spectroscopic and structural characterization, and thermal behavior. These methods collectively contribute to the robustness and reliability of the undertaken work.

BACKGROUND: Pituitary tumors are a heterogeneous group of lesions that are usually benign. Therefore, a proper understanding of the anatomy, physiology, and pathology is mandatory to achieve favorable outcomes. Accordingly, diagnostic tests and treatment guidelines should be determined and implemented. Thus, we decided to perform a multicenter study among Italian neurosurgical centers performing pituitary surgery to provide an actual depiction from the neurosurgical standpoint. METHODS: On behalf of the SINch (Società Italiana di Neurochirurgia), a survey was undertaken with the participants to explore the activities in the field of pituitary surgery within 41 public institutions. RESULTS: Of the 41 centers, 37 participated in the present study. The total number of neurosurgical procedures performed in 2016 was 1479. Most of the procedures were performed using the transsphenoidal approach (1320 transsphenoidal [1204 endoscopic, 53 microscopic, 53 endoscope-assisted microscopic] vs. 159 transcranial). A multidisciplinary tumor board is convened regularly in 32 of 37 centers, and a research laboratory is present in 18 centers. CONCLUSIONS: Diagnosing pituitary/hypothalamus disorders and treating them is the result of teamwork, composed of several diverse experts. Regarding neurosurgery, our findings have confirmed the central role of the transsphenoidal approach, with preference toward the endoscopic technique. Better outcomes can be expected at centers with a multidisciplinary team and a full, or part of a, residency program, with a greater surgical caseload.

Leksell Gamma Knife is a mini-invasive technique to obtain a complete destruction of cerebral lesions delivering a single high dose radiation beam. Positron Emission Tomography (PET) imaging is increasingly utilized for radiation treatment planning. Nevertheless, lesion volume delineation in PET datasets is challenging because of the low spatial resolution and high noise level of PET images. Nowadays, the biological target volume (BTV) is manually contoured on PET studies. This procedure is time expensive and operator-dependent. In this article, a fully automatic algorithm for the BTV delineation based on random walks (RW) on graphs is proposed. The results are compared with the outcomes of the original RW method, 40% thresholding method, region growing method, and fuzzy c-means clustering method. To validate the effectiveness of the proposed approach in a clinical environment, BTV segmentation on 18 patients with cerebral metastases is performed. Experimental results show that the segmentation algorithm is accurate and has real-time performance satisfying the physician requirements in a radiotherapy environment.

MR Imaging is being increasingly used in radiation treatment planning as well as for staging and assessing tumor response. Leksell Gamma Knife® is a device for stereotactic neuro-radiosurgery to deal with inaccessible or insufficiently treated lesions with traditional surgery or radiotherapy. The target to be treated with radiation beams is currently contoured through slice-by-slice manual segmentation on MR images. This procedure is time consuming and operator-dependent. Segmentation result repeatability may be ensured only by using automatic/semi-automatic methods with the clinicians supporting the planning phase. In this paper a semi-automatic segmentation method, based on an unsupervised Fuzzy C-Means clustering technique, is proposed. The presented approach allows for the target segmentation and its volume calculation. Segmentation tests on 5 MRI series were performed, using both area-based and distance-based metrics. The following average values have been obtained: DS = 95.10, JC = 90.82, TPF = 95.86, FNF = 2.18, MAD = 0.302, MAXD = 1.260, H = 1.636.

Nowadays, radiation treatment is beginning to intensively use MRI thanks to its greater ability to discriminate healthy and diseased soft-tissues. Leksell Gamma Knife® is a radio-surgical device, used to treat different brain lesions, which are often inaccessible for conventional surgery, such as benign or malignant tumors. Currently, the target to be treated with radiation therapy is contoured with slice-by-slice manual segmentation on MR datasets. This approach makes the segmentation procedure time consuming and operator-dependent. The repeatability of the tumor boundary delineation may be ensured only by using automatic or semiautomatic methods, supporting clinicians in the treatment planning phase. This article proposes a semiautomatic segmentation method, based on the unsupervised Fuzzy C-Means clustering algorithm. Our approach helps segment the target and automatically calculates the lesion volume. To evaluate the performance of the proposed approach, segmentation tests on 15 MR datasets were performed, using both area-based and distance-based metrics, obtaining the following average values: Similarity Index=95.59%, Jaccard Index=91.86%, Sensitivity=97.39%, Specificity=94.30%, Mean Absolute Distance=0.246[pixels], Maximum Distance=1.050[pixels], and Hausdorff Distance=1.365[pixels].

Lesion volume delineation of Positron Emission Tomography images is challenging because of the low spatial resolution and high noise level. Aim of this work is the development of an operator independent segmentation method of metabolic images. For this purpose, an algorithm for the biological tumor volume delineation based on random walks on graphs has been used. Twenty-four cerebral tumors are segmented to evaluate the functional follow-up after Gamma Knife radiotherapy treatment. Experimental results show that the segmentation algorithm is accurate and has real-time performance. In addition, it can reflect metabolic changes useful to evaluate radiotherapy response in treated patients.

Contains fulltext : 173083.pdf (Publisher’s version ) (Open Access), All laboratory tests have test performance characteristics (TPCs), whether or not they are explicitly known to the laboratorian or the pathologist. TPCs are thus also an integral characteristic of immunohistochemistry (IHC) tests and other in situ, cell-based molecular assays such as DNA or RNA in situ hybridization or aptamer-based testing. Because of their descriptive, in situ, cell-based nature, IHC tests have a limited repertoire of appropriate TPCs. Although only a few TPCs are relevant to IHC, proper selection of informative TPCs is nonetheless essential for the development of and adherence to appropriate quality assurance measures in the IHC laboratory. This paper describes the TPCs that are relevant to IHC testing and emphasizes the role of TPCs in the validation of IHC tests. This is part 2 of the 4-part series "Evolution of Quality Assurance for Clinical Immunohistochemistry in the Era of Precision Medicine."

Contains fulltext : 173020.pdf (Publisher’s version ) (Open Access), Technical progress in immunohistochemistry (IHC) as well as the increased utility of IHC for biomarker testing in precision medicine avails us of the opportunity to reassess clinical IHC as a laboratory test and its proper characterization as a special type of immunoassay. IHC, as used in current clinical applications, is a descriptive, qualitative, cell-based, usually nonlinear, in situ protein immunoassay, for which the readout of the results is principally performed by pathologists rather than by the instruments on which the immunoassay is performed. This modus operandi is in contrast to other assays where the instrument also performs the readout of the test result (eg, nephelometry readers, mass spectrometry readers, etc.). The readouts (results) of IHC tests are used either by pathologists for diagnostic purposes or by treating physicians (eg, oncologists) for patient management decisions, the need for further testing, or follow-up. This paper highlights the distinction between the original purpose for which an IHC test is developed and its subsequent clinical uses, as well as the role of pathologists in the analytical and postanalytical phases of IHC testing. This paper is the first of a 4-part series, under the general title of "Evolution of Quality Assurance for Clinical Immunohistochemistry in the Era of Precision Medicine."

Contains fulltext : 173084.pdf (Publisher’s version ) (Open Access), Validation of immunohistochemistry (IHC) assays is a subject that is of great importance to clinical practice as well as basic research and clinical trials. When applied to clinical practice and focused on patient safety, validation of IHC assays creates objective evidence that IHC assays used for patient care are "fit-for-purpose." Validation of IHC assays needs to be properly informed by and modeled to assess the purpose of the IHC assay, which will further determine what sphere of validation is required, as well as the scope, type, and tier of technical validation. These concepts will be defined in this review, part 3 of the 4-part series "Evolution of Quality Assurance for Clinical Immunohistochemistry in the Era of Precision Medicine."

Background and objectives: Nowadays, clinical practice in Gamma Knife treatments is generally based on MRI anatomical information alone. However, the joint use of MRI and PET images can be useful for considering both anatomical and metabolic information about the lesion to be treated. In this paper we present a co-segmentation method to integrate the segmented Biological Target Volume (BTV), using [11C]-Methionine-PET (MET-PET) images, and the segmented Gross Target Volume (GTV), on the respective co-registered MR images. The resulting volume gives enhanced brain tumor information to be used in stereotactic neuro-radiosurgery treatment planning. GTV often does not match entirely with BTV, which provides metabolic information about brain lesions. For this reason, PET imaging is valuable and it could be used to provide complementary information useful for treatment planning. In this way, BTV can be used to modify GTV, enhancing Clinical Target Volume (CTV) delineation. Methods: A novel fully automatic multimodal PET/MRI segmentation method for Leksell Gamma Knife® treatments is proposed. This approach improves and combines two computer-assisted and operator-independent single modality methods, previously developed and validated, to segment BTV and GTV from PET and MR images, respectively. In addition, the GTV is utilized to combine the superior contrast of PET images with the higher spatial resolution of MRI, obtaining a new BTV, called BTVMRI. A total of 19 brain metastatic tumors, undergone stereotactic neuro-radiosurgery, were retrospectively analyzed. A framework for the evaluation of multimodal PET/MRI segmentation is also presented. Overlap-based and spatial distance-based metrics were considered to quantify similarity concerning PET and MRI segmentation approaches. Statistics was also included to measure correlation among the different segmentation processes. Since it is not possible to define a gold-standard CTV according to both MRI and PET images without tre