CroSSED sequence, a new tool for 3D processing in geosciences using the free software 3DSlicer

Dorador, Javier; Rodríguez‐Tovar, Francisco J.

doi:10.1038/s41597-020-00614-y

Cited by 14 publications

(7 citation statements)

References 29 publications

(34 reference statements)

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“…Fusing imaging modalities ( 24 ) with no a priori knowledge or evidence about their optimal combination for the targeted therapy can lead to unnecessary, redundant analysis with a negative effect on the final decision. Thus, clinicians should provide insight and participate in cooperation with the data science engineers regarding specific lesion attributes concerning the followed diagnosis protocols.…”

Section: Development Of Radiomics Prediction Modelsmentioning

confidence: 99%

“…The critical issues of the robustness of quantitative features with respect to imaging variations and inter-institutional variability need to be investigated further. Currently, there are several advanced machines equipped with deep learningbased algorithms aimed at contour functions, including the 3DSlicer (24)(25)(26), DeepMind (Google) (27), and Project InnerEye (Microsoft) (https://www.microsoft.com/en-us/ research/project/medical-image-analysis/).…”

Section: Pre-processing Of Informationmentioning

confidence: 99%

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The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology

et al. 2021

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With the rapid development of new technologies, including artificial intelligence and genome sequencing, radiogenomics has emerged as a state-of-the-art science in the field of individualized medicine. Radiogenomics combines a large volume of quantitative data extracted from medical images with individual genomic phenotypes and constructs a prediction model through deep learning to stratify patients, guide therapeutic strategies, and evaluate clinical outcomes. Recent studies of various types of tumors demonstrate the predictive value of radiogenomics. And some of the issues in the radiogenomic analysis and the solutions from prior works are presented. Although the workflow criteria and international agreed guidelines for statistical methods need to be confirmed, radiogenomics represents a repeatable and cost-effective approach for the detection of continuous changes and is a promising surrogate for invasive interventions. Therefore, radiogenomics could facilitate computer-aided diagnosis, treatment, and prediction of the prognosis in patients with tumors in the routine clinical setting. Here, we summarize the integrated process of radiogenomics and introduce the crucial strategies and statistical algorithms involved in current studies.

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Section: Development Of Radiomics Prediction Modelsmentioning

confidence: 99%

Section: Pre-processing Of Informationmentioning

confidence: 99%

The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology

et al. 2021

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“…These 650 patients’ chest CT data were procured from the picture archiving and communication system (PACS). The software 3DSlicer [ 11 ]was used to identify and quantify muscle tissue [ 12 ], which allows for selective visualization of specific tissues, such as muscle or fat tissue, by setting density intervals representing the tissue of interest. According to the current literature, a density range of -29 to + 150 Hounsfield units (HU) was selected.…”

Section: Methodsmentioning

confidence: 99%

Preoperative sarcopenia combined with prognostic nutritional index predicts long-term prognosis of radical gastrectomy with advanced gastric cancer: a comprehensive analysis of two-center study

Han

Tan

et al. 2023

BMC Cancer

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Purpose This study aims to investigate the predictive value of the combined index smni(skeletal muscle index (SMI)-prognostic nutrition index(PNI)) for the postoperative survival of patients with advanced gastric cancer(AGC). Methods 650 patients with AGC from two centers (290 cases from the First Affiliated Hospital of Dalian University and 360 points from the Fujian Medical University Union Hospital) were selected as the study subjects based on unified screening criteria. Clinical data, preoperative abdominal CT images, results of hematology-related examinations, tumor-related characteristics, and surgical and follow-up data of the patients were collected and organized. The L3 vertebral level muscle area was measured using computer-assisted measurement techniques, and the skeletal muscle index(SMI) was calculated based on this measurement. The prognostic nutrition index (PNI) was calculated based on serum albumin and lymphocyte count indicators. The Kaplan-Meier survival analysis of data from the First Affiliated Hospital was used to determine that SMI and PNI are significantly correlated with the postoperative survival rate of patients with advanced gastric cancer. Based on this, a novel combined index smni was fitted and stratified for risk. Cox proportional hazards regression analysis was used to determine that the index smni is an independent prognostic risk factor for patients with AGC after surgery. The ROC curve was used to describe the predictive ability of the new combined index and its importance and predictive power in predicting postoperative survival of patients with AGC, which was verified in the data of Fujian Medical University Union Hospital. Result The Kaplan-Meier curve analysis of the combined indicator smni Is clearly associated with long-term survival(3-year OS (P < 0.001) and DSS (P < 0.001)), univariate analysis and multivariate analysis showed that smni was an independent prognostic risk factor, The ROC curve for the first center 3-year OS(AUC = 0.678), DSS(AUC = 0.662) show good predictive ability and were validated in the second center. Conclusion The combined index smni has a good predictive ability for the postoperative survival rate of patients with AGC and is expected to provide a new reference basis and more accurate and scientific guidance for the postoperative management and treatment of patients with AGC.

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“…The effectiveness of automatic segmentation depends on the algorithm’s precision and capacity to distinguish the ROIs from the surrounding tissues. Automatic segregation can be managed by a variety of technologies [ 45 , 46 , 47 ]. Because of this, numerous studies have demonstrated that semi-automatic segmentation is a more suitable method [ 48 ].…”

Section: Radiogenomics and Its Use In Precision Medicinementioning

confidence: 99%

The Convergence of Radiology and Genomics: Advancing Breast Cancer Diagnosis with Radiogenomics

Demetriou,

Lockhat,

Brzozowski

et al. 2024

Cancers

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Despite significant progress in the prevention, screening, diagnosis, prognosis, and therapy of breast cancer (BC), it remains a highly prevalent and life-threatening disease affecting millions worldwide. Molecular subtyping of BC is crucial for predictive and prognostic purposes due to the diverse clinical behaviors observed across various types. The molecular heterogeneity of BC poses uncertainties in its impact on diagnosis, prognosis, and treatment. Numerous studies have highlighted genetic and environmental differences between patients from different geographic regions, emphasizing the need for localized research. International studies have revealed that patients with African heritage are often diagnosed at a more advanced stage and exhibit poorer responses to treatment and lower survival rates. Despite these global findings, there is a dearth of in-depth studies focusing on communities in the African region. Early diagnosis and timely treatment are paramount to improving survival rates. In this context, radiogenomics emerges as a promising field within precision medicine. By associating genetic patterns with image attributes or features, radiogenomics has the potential to significantly improve early detection, prognosis, and diagnosis. It can provide valuable insights into potential treatment options and predict the likelihood of survival, progression, and relapse. Radiogenomics allows for visual features and genetic marker linkage that promises to eliminate the need for biopsy and sequencing. The application of radiogenomics not only contributes to advancing precision oncology and individualized patient treatment but also streamlines clinical workflows. This review aims to delve into the theoretical underpinnings of radiogenomics and explore its practical applications in the diagnosis, management, and treatment of BC and to put radiogenomics on a path towards fully integrated diagnostics.

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CroSSED sequence, a new tool for 3D processing in geosciences using the free software 3DSlicer

Cited by 14 publications

References 29 publications

The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology

The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology

Preoperative sarcopenia combined with prognostic nutritional index predicts long-term prognosis of radical gastrectomy with advanced gastric cancer: a comprehensive analysis of two-center study

The Convergence of Radiology and Genomics: Advancing Breast Cancer Diagnosis with Radiogenomics

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