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Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Confidence Intervals

Confidence Intervals

An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
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Related Experiment Video

Updated: Feb 15, 2026

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

Semi-automated pulmonary nodule interval segmentation using the NLST data.

Yoganand Balagurunathan¹, Andrew Beers², Jayashree Kalpathy-Cramer²

¹H.L.Moffitt Cancer Center (MCC), Tampa, FL, USA.

Medical Physics

|January 25, 2018

Summary

This summary is machine-generated.

Segmentation algorithms show variability in estimating pulmonary nodule volume changes, impacting malignancy prediction. Larger nodules (≥8mm) demonstrate higher measurement consistency and improved prediction accuracy across different algorithms.

Keywords:

CT lung change in volume segmentation lung nodule segmentation volume estimate

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Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

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Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

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Published on: May 19, 2023

Area of Science:

Medical Imaging
Quantitative Imaging
Pulmonary Nodule Analysis

Background:

Pulmonary nodules are common findings on low-dose computed tomography (LDCT).
Accurate assessment of nodule volume change is crucial for malignancy prediction.
Variability in segmentation algorithms can affect volume change estimates.

Purpose of the Study:

To assess the variability of pulmonary nodule volume change estimates across different segmentation algorithms.
To evaluate how segmentation variability impacts the prediction of nodule malignancy.

Main Methods:

Utilized 100 National Lung Screening Trial (NLST) LDCT datasets with nodules from two consecutive scans.
Five Quantitative Imaging Network (QIN) institutions performed semi-automated nodule segmentation.
Calculated nodule volume change (absolute and percent) and assessed malignancy prediction using logistic regression and AUROC, stratified by nodule size (<8mm and ≥8mm).

Main Results:

Substantial variability in volume change estimates was observed across algorithms (CCC 0.56-0.95).
Larger nodules (≥8mm) showed higher concordance (CCC 0.54-0.93) and improved malignancy prediction (AUC 0.75-0.90) compared to smaller nodules.
Malignancy prediction based on volume change was consistent across institutions, with AUCs ranging from 0.65-0.89.

Conclusions:

Segmentation concordance for nodule size measurements is higher for larger nodules (≥8mm).
Nodule volume change is a consistent predictor of malignancy across different segmentation algorithms and institutions.
Uncorrected volume change estimates may slightly reduce predictability.