Pituitary Adenoma: How Registry and Statistical Learning Can Improve Care and Reduce Costs
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Transcript of Pituitary Adenoma: How Registry and Statistical Learning Can Improve Care and Reduce Costs
Pituitary Adenoma: How Registry and Statistical Learning Can Improve Care and Reduce CostsWilliam McDonald, MDMedical Director, Abbott Northwestern Laboratory
Diagnosis as ClassificationCase Diagnosis Variable 1 Variable 2 Variable 3
1 Diagnosis x 14.1 Yes red
2 Diagnosis y 12.0 No blue
3 Diagnosis z 3.7 No blue
4 Diagnosis x 5.3 Yes red
… … … … …
Output variable Input variables
The pathologist studies these…
…to determine these.
Generally, the greater the number of diagnostic classes and potential input variables, absent GOLD STANDARDS, the more cases are needed to make the classification scheme.
Pituitary Adenoma• 10-15% of intracranial tumors• May produce anterior pituitary
hormones or be hormonally silent• Six anterior pituitary hormones have
corresponding IHC of variable sensitivity and specificity: Prolactin, Growth Hormone, ACTH, FSH, LH, TSH
How Many Classes of Adenoma?• Dependent upon
- Local customs- Available reagents- Availability of electron microscopy- Availability of experts
Why Care About Adenoma Class?• Medical therapy is available for some
adenomas- Dopamine agonists like bromocriptine are
used for prolactinomas - Somatostatin analogs or growth hormone
receptor antagonist Pegvisomant are used to treat growth hormone and TSH-producing tumors
- Pasireotide (a somatostatin-receptor binding agent) in Cushing’s disease
Pituitary Adenoma Classification• Founded on case series, early IHC
and electron microscopy• Complex: up to +/- 18 classes• Canonical literature flawed by
reliance on secondary and tertiary sources, varying class definitions, poor correlation with radiology, serology
Pituitary AdenomasDiagnosis Stain1 Stain2 Stain3 …
Densely granulated corticotroph
+ - - …
Sparsely granulated corticotroph
+ - - …
Crooke cell adenoma
+ - + …
Densely granulated somatotroph
- +/- + …
… … … …
Input variables
Many of these… … and many of these, with no clear gold standards.
Output variable
What Input Variables?• A informal poll at the 2007 American
Association of Neuropathologists annual meeting revealed great variability in the workup of pituitary adenoma
• Workups ranged from 1 H&E-stained section to 4+ IHC with additional electron microscopy in some cases
(unpublished data)
Possible Stains (Partial List)• Prolactin• Growth hormone• ACTH• LH• FSH• TSH• Pit-1• SF-1• Alpha subunit• Tpit
• CAM5.2• Alpha ER• Synaptophysin• Chromogranin• S100• PAS• p53• MIB1/Ki67• Others
Problem and Our Strategy• Many classes• Variable class
definitions• Many input
variables• No consensus on
workup• Limited resources
• Tabulate underlying biology and available IHC
• Tissue microarray for N• Correlation and
clustering techniques• Develop a screening
panel and algorithm• Stepwise testing of
algorithm with clinical correlation
Statistical Learning• Tools for understanding data• Emphasis on estimating an output based
on one or more inputs (supervised methods)
• Unsupervised methods used when output variable (tumor class, for instance) is unavailable
• Many software packages are now available• We used the free, open source program R
Diagnosis as ClassificationCase Diagnosis Variable 1 Variable 2 Variable 3
1 Diagnosis x 14 Yes red
2 Diagnosis y 12 No blue
3 Diagnosis z 3 No blue
4 Diagnosis x 5 Yes red
… … … … …
Output variable Input variables
The pathologist studies these…
…to determine these.
In most cases, though, we don’t have a GOLD STANDARD to help us establish an output variable. This is why we turned to unsupervised clustering techniques.
Unsupervised Clustering• Used when an output variable
unavailable• Means of data visualization• Clustering attempts to find
homogeneous subgroups among the observations
• Hierarchical clustering and K-means clustering are the most popular
Developing the IHC Panel• Biology: three families• Correlations, cluster analysis• Screen Step of SF-1, Pit-1, ACTH• A thought experiment: iterative
testing using TMA data• New algorithm compared with old
algorithm
The Algorithm• SF-1(+)Pit-1(-)ACTH(-)=Gonadotroph• SF-1(-)Pit-1(-)ACTH(-)=“Null”• SF-1(-)Pit-1(-)ACTH(+)=Corticotroph• Other patterns or clinical/serological
discrepancy add PRL,GH,TSH,Cam5.2
• Note: all diagnoses are checked against clinical and serological information before final diagnosis
Conclusions• IHC results show patterns consistent
with developmental biology of adenohypophysis
• Pit-1 and SF-1 are robust markers of lineage in pituitary adenomas, and can augment or replace several traditional stains
• Alpha subunit was not useful in our hands
Conclusions, continued• Unsupervised statistical learning methods
appear to group cases into biologically relevant groups that corresponded to available clinical information
• By using an algorithm produced by our work, we estimate that - Greater than one-third of cases have a
more accurate diagnosis- One-third fewer IHC are used than in
our previous system
Classification to Determine RxCase Treatment Prolactin,
serumHistologic
TypeRadiology Var4
1 Treatment x 14.1 Gon big …
2 Treatment y 12.0 ACTH small …
3 Treatment z 3.7 Null big …
4 Treatment x 5.3 PRL big …
… … … … …
Output variable Input variables
The team studies these……to determine these.
Acknowledgements• Abbott Northwestern Hospital Foundation• JNNI Neuroscience Research Division
- Nilanjana Banerji, PhD- Bridget Ho, CCRC
• Allina Health Neuroscience Clinical Service Line- Kim Radel, MHA
• U of Illinois at Chicago- Virgilia Macias, MD- Andre Kajdacsy-Balla, MD, PhD
• Jared Crotteau, RN; Michelle Stenbeck, RN• Kelsey McDonald, PhD
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Useful Web Resources• http://www.r-project.org• http://www.rstudio.com• https://google-styleguide.googlecode
.com/svn/trunk/Rguide.xml
• Ed Boone's R Channel on YouTube