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Related Concept Videos

Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics10:17

Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics

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We describe IBEX, an open-source tool designed for medical imaging radiomics studies, and how to use this tool. In addition, some published works that have used IBEX for uncertainty analysis and model building are...
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How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
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How Data are Classified: Categorical Data01:11

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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Data Reporting and Recording01:24

Data Reporting and Recording

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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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Data Collection I01:30

Data Collection I

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Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
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Data Validation01:03

Data Validation

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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
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Updated: Jan 20, 2026

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
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Radiomics: Data Are Also Images.

Mathieu Hatt1, Catherine Cheze Le Rest2,3, Florent Tixier2

  • 1LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France; and hatt@univ-brest.fr.

Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine
|September 5, 2019
PubMed
Summary
This summary is machine-generated.

This review updates on radiomics in nuclear medicine imaging and oncology. Key challenges in study design, data acquisition, and analysis are addressed with solutions, including deep learning, to improve radiomics research quality and reproducibility.

Keywords:
deep learningmachine learningradiomics

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Area of Science:

  • Radiology
  • Nuclear Medicine
  • Oncology

Background:

  • Radiomics is rapidly advancing in nuclear medicine and oncology.
  • Significant challenges exist in study design, data acquisition, segmentation, feature calculation, and modeling.
  • Reproducibility and quality of radiomics studies require improvement.

Purpose of the Study:

  • To provide an update on the state of the art in radiomics for nuclear medicine and oncology.
  • To identify and discuss pitfalls in radiomics research.
  • To present solutions, future perspectives, and challenges in the field.

Main Methods:

  • Review of current literature on radiomics in nuclear medicine and oncology.
  • Identification of common pitfalls in radiomics workflows.
  • Exploration of potential solutions and emerging techniques, including deep learning.

Main Results:

  • Pitfalls were identified across study design, data acquisition, segmentation, feature calculation, and modeling.
  • Potential solutions and existing recommendations can address many identified pitfalls.
  • Deep learning techniques show promise for automating radiomics processes.

Conclusions:

  • Advances in radiomics have been substantial over the last five years.
  • Adhering to recommendations and employing solutions can enhance radiomics study quality and reproducibility.
  • Addressing remaining challenges is crucial for the continued evolution of radiomics in oncology.