1,720,976 research outputs found
Normalization strategies in multi-center radiomics abdominal MRI: systematic review and meta-analyses
Get PDFGoal: Artificial intelligence applied to medical image analysis has been extensively used to develop non-invasive diagnostic and prognostic signatures. However, these imaging biomarkers should be largely validated on multi-center datasets to prove their robustness before they can be introduced into clinical practice. The main challenge is represented by the great and unavoidable image variability which is usually addressed using different pre-processing techniques including spatial, intensity and feature normalization. The purpose of this study is to systematically summarize normalization methods and to evaluate their correlation with the radiomics model performances through meta-analyses. This review is carried out according to the PRISMA statement: 4777 papers were collected, but only 74 were included. Two meta-analyses were carried out according to two clinical aims: characterization and prediction of response. Findings of this review demonstrated that there are some commonly used normalization approaches, but not a commonly agreed pipeline that can allow to improve performance and to bridge the gap between bench and bedside
CT texture analysis to predict response to target therapy of hepatic metastases from colorectal cancer
No full textIntroduction Colorectal cancer (CRC), the 2nd cause of cancer death worldwide, is an indolent disease with 50% of patients eventually developing liver metastases (mCRC). Repeated cycles of different chemotherapies, combined with surgery in oligo-metastatic cases, are the therapeutic standard in mCRC. However, this strategy is resolutive in less than 15% of cases. Differentiating non- and short-term responders from potentially “cured” patients will spare patients needless toxicity and allow alternative treatments earlier, with conceivable cost and life savings. In this study we aimed to use CT texture analysis (CTTA) to identify specific imaging biomarkers of hepatic metastases, able to predict patient’s response to therapy and overall survival.
Methods We exploited the imaging data-set of the HERACLES trial (NCT03225937): 23 patients with amplified Human Epidermal growth factor Receptor 2 (HER2) mCRC were included in the study. All had received anti HER2 treatment, and underwent CT examination every 8 weeks, until disease progression. CT scans were semi-automatically segmented to extract for each patient all liver metastases. Texture analysis was performed on each segmented area, computing for each lesion 34 quantitative parameters. Both mono-parametric and multi-parametric analysis were assessed to identify features most correlated to therapy response. We also performed a correlative survival (OS) analysis, considering subjects with good survival those with OS > 9 months.
Results In 23 patients we found 124 metastases, 55 of which were classified as responding and 69 as non-responding. Nine parameters reached statistical significance in the mono-parametric analysis (best AUC=0.67, p=0.001), while in the multivariate regression ten parameters were used in the model, achieving and AUC equal to 0.82, with sensitivity of 82% and specificity 72%. For OS analysis, 12 patients were “good” and 11 “poor” survivors. In the mono-parametric analysis “cluster prominence” and “sum entropy” predicted OS with AUC equal to 0.78 and 0.83, respectively. The regression model with two variables (“cluster prominence” and “dissimilarity”) reached a sensitivity of 83% and a specificity of 82%.
Conclusions Our study demonstrated CTTA as a potential biomarker to predict response of hepatic metastases to chemotherapy treatment, possibly saving patients predicted as non-responder from toxicity. Moreover, CTTA could give indications on patients OS, without the need for additional tests
Virtual biopsy in abdominal pathology: where do we stand?
Get PDFIn recent years, researchers have explored new ways to obtain information from pathological tissues, also exploring non-invasive techniques, such as virtual biopsy (VB). VB can be defined as a test that provides promising outcomes compared to traditional biopsy by extracting quantitative information from radiological images not accessible through traditional visual inspection. Data are processed in such a way that they can be correlated with the patient’s phenotypic expression, or with molecular patterns and mutations, creating a bridge between traditional radiology, pathology, genomics, and artificial intelligence (AI). Radiomics is the backbone of VB, since it allows the extraction and selection of features from radiological images, feeding them into AI models in order to derive lesions' pathological characteristics and molecular status. Presently, the output of VB provides only a gross approximation of the findings of tissue biopsy. However, in the future, with the improvement of imaging resolution and processing techniques, VB could partially substitute the classical surgical or percutaneous biopsy, with the advantage of being non-invasive, comprehensive, accounting for lesion heterogeneity, and low cost. In this review, we investigate the concept of VB in abdominal pathology, focusing on its pipeline development and potential benefits
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study
Get PDFBackground: Pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal
cancer (LARC) is achieved in 15–30% of cases. Our aim was to implement and externally validate a magnetic
resonance imaging (MRI)-based radiomics pipeline to predict response to treatment and to investigate the impact
of manual and automatic segmentations on the radiomics models.
Methods: Ninety-five patients with stage II/III LARC who underwent multiparametric MRI before
chemoradiotherapy and surgical treatment were enrolled from three institutions. Patients were classified as
responders if tumour regression grade was 1 or 2 and nonresponders otherwise. Sixty-seven patients composed the
construction dataset, while 28 the external validation. Tumour volumes were manually and automatically
segmented using a U-net algorithm. Three approaches for feature selection were tested and combined with four
machine learning classifiers.
Results: Using manual segmentation, the best result reached an accuracy of 68% on the validation set, with
sensitivity 60%, specificity 77%, negative predictive value (NPV) 63%, and positive predictive value (PPV) 75%. The
automatic segmentation achieved an accuracy of 75% on the validation set, with sensitivity 80%, specificity 69%,
and both NPV and PPV 75%. Sensitivity and NPV on the validation set were significantly higher (p = 0.047) for the
automatic versus manual segmentation.
Conclusion: Our study showed that radiomics models can pave the way to help clinicians in the prediction of
tumour response to chemoradiotherapy of LARC and to personalise per-patient treatment. The results from the
external validation dataset are promising for further research into radiomics approaches using both manual and
automatic segmentations
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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