Volumetric Measurement of Tumors David F. Yankelevitz, MD.

Slides:

Advertisements

Similar presentations

Vision and Image Analysis Group (VIA) Anthony P. Reeves School of Electrical and Computer Engineering Cornell University © A. P. Reeves 2007.

Advertisements

Computed Tomography II

Spiral CT Bushong Chapter 5.

Seeram Chapter 13: Single Slice Spiral - Helical CT

Automatic Segmentation and Volume Estimation of Lung Cancer Nodules using Computed Tomography Timothy R. Tuinstra, PhD Candidate.

ACR and SBI Statement Margarita Zuley, MD Associate Professor, Radiology Medical Director, Breast Imaging University of Pittsburgh.

Computed Tomography III

Response Assessment Criteria for Clinical Trials Tumor Imaging Metrics Core January 29, 2008 Cheryl A. Sadow, MD Abdominal Imaging & Intervention Division.

Dmitriy Zinovev. Summary 51 radiology reports 77 malignant cases 63 guesses.

Arizona’s First University. Feasibility of Image-Guided SRS for Trigeminal Neuralgia with Novalis.

A study on the effect of imaging acquisition parameters on lung nodule image interpretation Presenters: Shirley Yu (University of Southern California)

Figure 1: Locations of rosette strain gauges (n = 10) on the cadaveric pelvis. * * * * * * * * * * G Figure 3: Fixture for loading the pelvis (A) actuator,

Special Imaging Techniques Chapter 6 Bushong. Dynamic Computed Tomography (DCT) Dynamic scanning implies 15 or more scans in rapid sequence within one.

SPIE Medical Imaging Conference Lung Imaging Database Consortium (LIDC) LIDC Data Elements and Data Collection Process February 13, 2005.

Screen discovered nodules: What next? Anil Vachani, MD, MS Assistant Professor of Medicine Director, Lung Nodule Program University of Pennsylvania Medical.

COMPUTED TOMOGRAPHY I – RAD 365 CT - Scan

Mr Will Finch MBBS BSc(Hons) MRCS Urology SpR Edith Cavell Hospital.

بسم الله الرحمن الرحيم و قل رب زدنى علماً ﴿و قل رب زدنى علماً﴾ صدق الله العظيم.

CT Quality Control for CT Scanners. Quality Control in CT A good idea? Yes Required for accreditation? Sometimes Improves image quality? Sometimes Depends.

QIBA CT Volumetrics - Cross-Platform Study (Group 1C) May 6, 2009 Interclinic Comparison of CT Volumetry Quantitative Imaging Biomarker Alliance.

Update on Lung Cancer Image Processing Rick Avila Karthik Krishnan Luis Ibanez Kitware, Inc. April 19, 2006.

QIBA CT Volumetrics - Cross-Platform Study (Group 1C) December 23, 2009 CT Cross-platform Sizing of Phantom Lesions Quantitative Imaging Biomarker Alliance.

Evaluation of the Performance of the Fast Scanning Platform of an OCT System Malcolm Heard 1, Miguel Herrera 1, Geoffrey Ibbott 1 1 Department of Radiation.

1 SEGMENTATION OF BREAST TUMOR IN THREE- DIMENSIONAL ULTRASOUND IMAGES USING THREE- DIMENSIONAL DISCRETE ACTIVE CONTOUR MODEL Ultrasound in Med. & Biol.,

Slide - 1 Confidential. Slide - 2 Confidential Slide - 3 Confidential Interalgorithm Study using CT Images of synthetic nodules……….

Muscle Volume Analysis 3D reconstruction allows for accurate volume calculation Provides methods for monitoring disease progression Measure muscle atrophy.

CT physics and instrumentation

WORK UP & MANAGEMENT OF SOLITARY PULMONARY NODULE Seifu B Oct-04, 2007.

RECIST Overview.

S Demehri 1, M.K Kalra 2, M.L Steigner 1, F.J Rybicki 1, M.J. Lang, 3, S.G Silverman 1. 1.Department of Radiology, Brigham & Women's Hospital, Harvard.

ASSESSMENT OF INTERNAL BRUISE VOLUME OF SELECTED FRUITS USING MR IMAGING Ta-Te Lin, Yu-Che Cheng, Jen-Fang Yu Department of Bio-Industrial Mechatronics.

QIBA CT Volumetrics - Cross-Platform Study (Group 1C) September 2, 2009 Interclinical Comparison of CT Volumetry Quantitative Imaging Biomarker Alliance.

QIBA CT Volumetrics Group 1B: (Patient Image Datasets) Update April 19, 2011.

QIBA CT Volumetrics Group 1B: (Patient Image Datasets)

References Adherence to follow-up CT scans in patients with small pulmonary nodules, a retrospective study Sofie Lock Johansson, Niels-Chr. G. Hansen Department.

CAD Performance Analysis for Pulmonary Nodule Detection: Comparison of Thick- and Thin-Slice Helical CT Scans Randy D Ernst 1, Russell C Hardie 2, Metin.

QIBA CT Volumetrics - Cross-Platform Study (Group 1C) March 18, 2009 Interclinic Comparison of CT Volumetry Quantitative Imaging Biomarker Alliance.

QIBA CT Volumetrics Group 1B: (Patient Image Datasets) Teleconference Nov 5, 2008.

Conclusions Simulated fMRI phantoms with real motion and realistic susceptibility artifacts have been generated and tested using SPM2. Image distortion.

Part No...., Module No....Lesson No

Quality Control Bushong Chapter 10.

Computer-aided diagnosis in lung CT image analysis The state of the art in pulmonary CAD design and implementation. Matteo Santoro.

Introduction to Medical Imaging Week 3: Introduction to Medical Imaging Week 3: CT – Reconstruction and uses Guy Gilboa Course

Computed Tomography Computed Tomography is the most significant development in radiology in the past 40 years. MRI and Ultrasound are also significant.

The Optimization of Reconstruction Method Reducing Partial Volume Effect in PET/CT 3D Image Acquisition Department of Nuclear Medicine, Samsung Medical.

Boksoon Chang, MD ; Jung Hye Hwang, MD ; Yoon-Ho Choi, MD ; Man Pyo Chung, MD, PhD ; Hojoong Kim, MD, PhD, FCCP ; O Jung Kwon, MD, PhD ; Ho Yun Lee, MD.

Computed tomography. Formation of a CT image Data acquisitionImage reconstruction Image display, manipulation Storage, communication And recording.

University of Pennsylvania Department of Orthopaedic Surgery Joseph King, Eileen Crawford, Abass Alavi, Arthur Staddon, Lee Hartner, Richard Lackman and.

IMAGE QUALITY. SPATIAL RESOLUTION CONTRAST RESOLUTION NOISE IMAGE ARTIFACTS RADIATION DOSE.

Dr. Malhar Patel DNB (Radiation Oncology)

Automated airway evaluation system for multi-slice computed tomography using airway lumen diameter, airway wall thickness and broncho-arterial ratio B.

Physica Medica 32 (2016) 1570–1574 報告人：王俊淵

CT Multi-Slice CT.

Validation and Evaluation of Algorithms

Impact of Obesity on Medical Imaging and Image-Guided Intervention

Keith E. Kelly, MD and William H. Culbertson, MD

Can Renal Mass Features on CT Predict Positive Margins?

Retrospective Analysis of Risk Assessment of Multiple factors Affecting Transthoracic CT-Guided Lung Biopsy Munir, Sohaib1; Flood, Justin1; Nolan, Robert1;

Advantages of spiral CT

CT dose optimization software synergy – A clinical perspective

Preoperative Localization of Lung Nodules With Fiducial Markers: Feasibility and Technical Considerations Amita Sharma, MD, Shaunagh McDermott, MD, Douglas.

The Lung Reporting and Data System (LU-RADS): A Proposal for Computed Tomography Screening Daria Manos, MD, Jean M. Seely, MD, Jana Taylor, MD, Joy Borgaonkar,

Difficulties encountered managing nodules detected during a computed tomography lung cancer screening program Giulia Veronesi, MD, Massimo Bellomi, MD,

Volume 65, Issue 4, Pages (April 2014)

Quantitative Discrimination of Pigmented Lesions Using Three-Dimensional High- Resolution Ultrasound Reflex Transmission Imaging Deepak Rallan, Nigel.

Volume evaluation during follow-up allows the detection of nodule growth over a shorter period of time compared to diameter estimation. a) Computed tomography.

A novel technique for localization and excisional biopsy of small or Ill-defined pulmonary lesions Thomas M Daniel, MD, Talissa A Altes, MD, Patrice.

Journal of Thoracic Oncology

Computed Tomography (C.T)

Presentation transcript:

Volumetric Measurement of Tumors David F. Yankelevitz, MD

Why Measure Tumor Volumes? Surrogate for knowing the amount of viable tumor Implied is this: –Larger volumes, therefore progression –Smaller volumes, therefore response

How do we measure volumes? Surrogates –Uni-dimension (RECIST) –Bi-dimension (WHO) –Tri-dimension Genuine volume measurements

Advantages of Volume Measurements Greater proportional change –26% diameter increase corresponds to 100% volume increase Measurement of asymmetric growth Tumor volume doubling time

Days to ERCT from initial CT Initial nodule diameter (mm) Doubling time (days) (24 vs 93%) (4 vs 12%) Expected Change in Diameter

Asymmetric Growth SPN (6.9 mm) at baseline and 36 days later Virtually unchanged according to 2D metrics Apparently benign (DT=9700) Area: 36.5 mm 2 Perimeter: 22.7 mm Length: 8.27 mm Width: 5.62 mm Area: 36.6 mm 2 Perimeter: 23.4 mm Length: 8.23 mm Width: 5.66 mm

Volumetric Analysis 3D analysis reveals significant growth along scanner axis! (DT = 104, malignant)

8 mm Stable Nodule

Volumetric Growth Rate Analysis 8 mm stable pulmonary nodule at baseline and 181 days later MVGI = 0.57%

10 mm Malignant Nodule

Volumetric Growth Rate Analysis 10 mm malignant pulmonary nodule at baseline and 32 days later MVGI = 22.0% -- Squamous Cell Carcinoma

Inputs Into Volume Estimates Accuracy of measuring device (machine) –Inplane (x,y), out of plane (z) Ability to define borders of target (anatomic) –Removal of attached structures CAD –Defining edges Margin of tumor Adjacent edema/inflammation –Stability of structures

10mm Slice Thickness (Anisotropic) © ELCAP 2002

5mm Slice Thickness © ELCAP 2002

2.5mm Slice Thickness © ELCAP 2002

1mm Slice Thickness © ELCAP 2002

Headline Courtesy of University of Erlangen, Department of Radiology and Institute of Medical Physics SOMATOM Sensation 64 6 sec for 400 mm 64 x 0.6mm (2x32) Resolution 0.4 mm Rotation 0.37 sec 120 kV / 100 mAs

Headline Courtesy of University of Erlangen, Department of Radiology and Institute of Medical Physics SOMATOM Sensation 64 6 sec for 400 mm 64 x 0.6mm (2x32) Resolution 0.4 mm Rotation 0.37 sec 120 kV / 100 mAs

Volumetric CT Scanning

Accuracy of Area Measurements

Deformable Synthetic Nodules

Volumetric Measurement - Synthetic Nodules Volume Error:(3-6 mm) = 1.1% RMS, 2.8% max (6-11 mm) = 0.5% RMS, 0.9% max Function of nodule size Yankelevitz, et al. Radiology 2000

Removal of Attached Structures Jan , (X,Y) resolution: mm, Slice thickness : 1 mm Images ©1999, ELCAP Lab, Weill Medical College of Cornell University

Solid Nodule Segmentation

Images ©1999, ELCAP Lab, Weill Medical College of Cornell University 74-Day Doubling Time Volumetric Doubling Time Estimation

Limitations of Segmentation

Part-Solid Nodule: Complex Segmentation

Abutting Pleura: Limitations of Segmentation

Nonsolid Nodule: Indistinct Border

Less Natural Contrast

SPICULATED NODULE Instructions to Thoracic Radiologists were “Draw the Boundary of the Nodule”

SPICULATED NODULE Expert Number 1 Contour

SPICULATED NODULE Expert Number 2 Contour

SPICULATED NODULE Comparison of Contours

Motion Artifact – Patient Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Patient Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Patient Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Patient Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Patient Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Patient Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Patient Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Patient Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Patient Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Patient Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Patient Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Patient Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Patient Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Nonsolid nodule: Adenocarcinoma, bronchioloalveolar subtype

Motion Artifact – Cardiac Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Cardiac Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Cardiac Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Cardiac Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Cardiac Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Cardiac Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Cardiac Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Cardiac Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Cardiac Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Cardiac Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Cardiac Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Cardiac Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Cardiac Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Cardiac Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Cardiac Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Cardiac Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Cardiac Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Cardiac Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Cardiac Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Cardiac Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Cardiac Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Cardiac Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Motion Artifact – Cardiac Motion Images © , ELCAP Lab, Weill Medical College of Cornell University

Nodule Growth Rates Exponential Growth Model Nodule Doubling Time (DT) Traditional 2D Approximation

Appropriate Time to Follow-up CT When should the follow-up CT be done? where  d  is the reliably-detectable percent volume change, a function of initial nodule size  d  = two standard deviations of PVC in stable nodules by size category DT D = 400 days for baseline cases DT D = upper bound on doubling time for repeat cases example: 208 days for 3 mm nodule

Time to Follow-up CT Appropriate time to follow-up CT by initial nodule size detected on baseline or repeat screening Time to Follow-up CT (days) for nodules detected on Size (mm)  (d) (%)BaselineRepeat

Review of Literature Limited data on comparison of 3D volume measurements to 2D or 1D, notably for large lesions Most report that volume is better for large ‘well-defined’ abnormalities Limited impact on change in category for RECIST

Summary Technology has greatly improved –Measuring device –Image processing Little work has been done in regard to complex abnormalities Potential to markedly improve response estimates

Volumetric Measurement of In Vivo Nodules Although we had quantified the relative error in phantom nodule measurement by size, the error for in vivo nodules must be greater –partial volume –vascular geometry –motion artifacts

Assessment of In Vivo Volume Estimates Rescanning in short interval –Smallest change in true nodule volume –Difficult study due to dose concerns Stable nodules –Scans more easily obtained (screening) –Accounts for small errors in patient positioning and scanner calibration drift

Cases 262 HRCT scans of 120 stable nodules –Standard dose, small FOV, HRCT –Nodules 2-11 mm in diameter –Determination of stability based on radiologist evaluation over period of 2 or more years –Assessment of technical artifacts Incomplete acquisition System error –Assessment of motion artifacts Five-point scale Patient motion (gross movement, respiration) Cardiac motion 20 HRCT scans of 10 malignant nodules

Artifacts Motion artifact and technical artifact in 262 CT scans by initial nodule size Technical Motion Artifact ScoreAny Artifact NoneMinimalModeratePronouncedSevere Artifact Size (mm) Any size (22%) of cases had to be excluded due to technical or motion artifacts

Stable Nodules Frequency distribution of 94 stable nodules by initial size and time to follow-up CT Time to Follow-up CT (months) Any Interval Size (mm) Any size

Monthly Volumetric Growth Index Monthly Volumetric Growth Index, MVGI –Percent change in volume per month –Remaps growth estimates into two distinct classes Reeves, et al. RSNA 2001

Nodule Growth Rates MVGI = 32.5 MVGI = 15.1 MVGI = 4.3

MVGI of Stable Nodules Mean and standard deviation of monthly volumetric growth index of 94 stable nodules by initial size and time to follow-up CT Time to Follow-up CT (months) Any Interval Size (mm) MeanSDMeanSDMeanSDMeanSD Any Size Overall mean 0.06% Std. Err. of the Mean 0.21%

MVGI of Stable Nodules Mean and standard deviation of monthly volumetric growth index of 94 stable nodules by initial size and time to follow-up CT Time to Follow-up CT (months) Any Interval Size (mm) MeanSDMeanSDMeanSDMeanSD Any Size SD decreases with increasing size SD decreases with increasing time to follow-up CT

PVC of Stable Nodules Mean and standard deviation of percent volume change of 94 stable nodules by initial size and time to follow-up CT Time to Follow-up CT (months) Any Interval Size (mm) MeanSDMeanSDMeanSDMeanSD Any Size

PVC of Stable Nodules Mean and standard deviation of percent volume change of 94 stable nodules by initial size and time to follow-up CT Time to Follow-up CT (months) Any Interval Size (mm) MeanSDMeanSDMeanSDMeanSD Any Size SD decreases with increasing size SD increases with increasing time to follow-up CT

Malignant Nodules Monthly volumetric growth index of 10 malignant nodules with initial size, time to follow-up CT, and histologic diagnosis Initial Time to Follow-upHistologic CaseDetectionSize (mm)CT (days)MVGI (%)Diagnosis 1 Baseline Adenocarcinoma 2 Baseline Squamous Cell 3 Baseline Large Cell 4 Baseline Adenocarcinoma 5 Repeat Adenocarcinoma 6 Repeat Squamous Cell 7 Repeat Adenocarcinoma 8 Repeat Adenocarcinoma 9 Repeat Adenocarcinoma 10 Repeat Large Cell

Comparison of MVGI Values All of the stable nodules had values within two standard deviations of the corresponding mean value by size, while each of the 10 malignant nodules exceeded that corresponding value.

Conclusions The mean value of MVGI for stable nodules was 0.06% and its standard error was 0.21%. All of the stable nodules had values within two standard deviations of the corresponding mean value by size, while each of the 10 malignant nodules exceeded that corresponding value. Conclusion: Three-dimensional computer methods can be used to reliably characterize growth in small solid pulmonary nodules. Factors affecting the reproducibility of growth rate estimates include the initial nodule size, the timing of the follow-up scan, and the presence of patient-induced or technical artifacts.