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Tumor Measurement Criteria milestones - 1981 & 2000. RESPONSE EVALUATION CRITERIA IN SOLID TUMORS (RECIST) New Guidelines to Evaluate the Response to Treatment in Solid Tumors

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tumor measurement criteria milestones 1981 2000
Tumor Measurement Criteriamilestones -1981 & 2000


New Guidelines to Evaluate the Response to Treatment in Solid Tumors

P Therasse, SG Arbuck, EA Eisenhauer,J Wanders, RS Kaplan, L Rubinstein, J Verweij, M Van Glabbeke, AT van Oosterom, MC Christian, SG Gwyther

Journal of the National Cancer Institute 92: 205-216, 2000


WHO Handbook for Reporting Results of Cancer Treatment

World Health Organization Offset Publication No. 48Geneva, Switzerland, 1979


Reporting Results of Cancer Treatment

AB Miller, B Hogestraeten, M Staquet, A Winkler

Cancer 47:207–14, 1981

recist criteria r esponse e valuation c riteria i n s olid t umors

RECIST CriteriaResponse Evaluation Criteria In Solid Tumors

Simplification of former methods

4 response categories (CR, PR, PD, SD)

Based on linear 1-D being as good as 2-D

Least effort, conservative, for widest acceptance


RECIST Criteria

  • CR = disappearance of all target lesions
  • PR = 30% decrease in the sum of the longest diameter of target lesions
  • PD = 20% increase in the sum of the longest diameter of target lesions
  • SD = small changes that don’t meet above criteria

CR = complete response

PR = partial response

PD = progressive disease

SD = stable disease


RECIST criteria

‘Target’ lesions

  • All measurable lesions up to a maximum of five lesions per organ, and 10 lesions in total
  • Sum of the longest diameter of all of the target lesions
  • RECIST criteria may be employed by NCI-funded cooperative groups which are encouraged, but not required, to use
  • RECIST criteria are a voluntary, international standard, and not an NCI standard
  • That doesn’t mean Clinical Trial groups are satisfied with it


20 weeks (PR at - 39%)

24 weeks (PR confirmed - 52%)

52 weeks (- 74%)

metastatic renal cell



13 wks (– 7 %)

27 wks (PR – 43 %)

metastatic renal cell

the value of image data
The Value of Image Data

Validated image data could lead to:

  • Smaller clinical trials with fewer patients
  • Earlier go/no decisions on compounds
  • Faster regulatory approval
  • Shorter time to market
  • a measurable characteristic that predicts a clinical endpoint
  • “surrogate marker” is a biomarker that substitutes for a clinical endpoint
    • “surrogate marker” is a special case biomarker, i.e, not just a predictor of a clinical endpoint, but a reliable substitute for a clinical endpoint
      • the distinction has regulatory implications
  • Outcome data is needed to establish validity of a surrogate marker
first steps
First steps
  • Appropriate, disease-sensitive imaging
  • Uniformly acquired with objective QA
  • Quantitatively assessed
  • Centrally accessible with metadata
lung nodule volume growth
Lung nodule volume growth

Time Difference = 130 days

linear dimension increased 8 mm -> 11 mm in 4 months

A.P.Reeves, Cornell University, 1999

why not calculate volumes
Why not calculate volumes?
  • No fully automatic, objective methods
  • Semi-automatic methods are time-consuming, labor-intensive, and/or not user-friendly.
non cytoreductive i e functional measures
“Non-cytoreductive”(i.e. functional) measures
  • MR spectroscopy
  • CT density and contrast dynamics
  • Future:
    • Other PET ligands
    • Macromolecular MR agents
    • Optical methods
pet ct hybrid pet ct for gist response to imatinib gleevec
PET, CT, hybrid PET/CT forGIST response to imatinib (Gleevec)


7 wks post rx

G. W. Goerres et al, Univ Hosp Zurich

visual subjective standardized uptake value suv semi quantitative kinetic analysis quantitative
Visual: subjectiveStandardized Uptake Value (SUV): semi-quantitativeKinetic analysis: quantitative

Concerns about assessing 18FDG uptake in malignant tissue:


DCE MRI VEGF Inhibition time after contrast bolus (PTK/ZK TK inhibitor oral dose results on colon mets)

Morgan B et al, JCO 2003


Chemotherapy Response by MRI & MRS

1 wk


76 cc

Day 1

AC x1

79 cc

Day 42

AC x3

26 cc

Day 70

AC x4

25 cc

Day 112

taxol x2

11 cc

Day 178

taxol x4

6 cc







partial response to AC, regrowth on taxol

final pathology - viable IDC and extensive DCIS

Univ. of Minnesota

nci fda interagency oncology task force
NCI-FDA Interagency Oncology Task Force
  • Imaging Science Development for Oncologic Applications – Work in Progress
    • Develop volumetric anatomical imaging for oncology e.g. revise (RECIST)
    • Develop standard dynamic (contrast) imaging techniques for oncologic drug development and as surrogate endpoint for drug approvals
    • Validate FDG-PET for oncologic drug development and as a surrogate endpoint for drug approvals
    • Develop a pathway for accelerating molecular imaging including ‘first in human’ studies in diagnosed cancer patients
foci on imaging
Foci on imaging
  • NCI:Development and optimization of cancer specificCAD methods
  • NIBIB: Development of advanced algorithms and generic image processing methods, code documentation, open source software.
  • NLM: Open source software and related data processing platforms.
  • NSF: Advanced algorithm development, specialized hardware, GRID computing resources.
  • FDA: Development of standards for database development and
  • NIST:Measurement of performance of application specific software.
imaging methods validated as cancer biomarkers
Imaging methods validated as cancer biomarkers.
  • Objectives:
  • Increase imaging studies, using standardized acquisition protocols, in NCI-funded therapy trials
  • Collate imaging data from all NCI-funded trials, e.g., in Cancer Centers, Cooperative Groups, CCR, etc.
  • Engage FDA through Inter Organization Task Force
  • Develop cadre of oncology imaging specialists in Cancer Centers
  • Develop functional imaging committees in all Cooperative Groups
  • Develop volumetric and functional “RECIST” criteria
cip near term goals data collection develop validated data collections
CIP Near Term Goals: Data CollectionDevelop validated data collections:
  • Lung nodules (FNIH Demonstration Project)
    • for Detection, Classification, rx. Response
  • Liver mets - rx response
  • Colon polyps - screening detection, classification
  • Breast digital mammo - detection, classification
clinical imaging concerns
Clinical Imaging Concerns
  • Only 2% of all cancer patients are in formal clinical trials
  • Unless genetics is found to be deterministic, (all) cancer therapy will continue to be experimental
  • Conventional diagnostic imaging provides (barely quantitative) information when following a course of therapy