An introduction to interpretable machine learning in endocrinology. In particular, the application of Generalized Matrix Relevance LVQ to the classification of andrenocortical tumors and the differential diagnosis of primary aldosteronism is given.

1. Interpretable machine learning in endocrinology
•tumor classification •diagnosis of primary aldosteronism
Michael Biehl
www.cs.rug.nl/~biehl
Bernoulli Ins6tute for Mathema+cs,
Computer Science and Ar+ficial Intelligence
University of Groningen, The Netherlands
Centre for Systems Modelling &
Quan6ta6ve Biomedicine
University of Birmingham, UK
January 2024

2. 1
Steroid metabolomics
- classifica+on of adrenocor6cal tumors
- differen+al diagnosis of Primary Aldosteronism
- other applica+ons
Summary / Remarks
overview
Generalized Matrix Relevance Learning Vector Quan6za6on
- interpretable, prototype- & distance-based classifica+on
- relevance learning
The mysterious learning machine
© C.M. Gerigk, E.L. van den Brandhof
shallow systems
small data

3. • training: represent data by one or
several prototypes per class
• working: classify a query according to
the label of the nearest prototype (± bias)
• decision boundaries according
to (Euclidean) distances
+ parameterized in feature space,
intuitive and interpretable
one intuitive, interpretable framework:
prototype-based systems for distance-based classification
Learning Vector Quan1za1on (LVQ)
N-dim. feature space
?
x1
x2
2

4. distance measure compares
prototypes
data points
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d(w, x)
<latexit sha1_base64="BO0hjh0jjGtOWhN24FvEx5iLYjA=">AAACG3icbVDLSgMxFM3UV62v+ti5CRahQikzRdGNUHDjqlSxD+jUIZNm2tBMZkgyljLMf7jxV9y4UMSV4MK/Me10oa0HAodzziX3HjdkVCrT/DYyS8srq2vZ9dzG5tb2Tn53rymDSGDSwAELRNtFkjDKSUNRxUg7FAT5LiMtd3g18VsPREga8Ds1DknXR31OPYqR0pKTr9g+UgPXi0fJZXHkWKWRUynZrBcoCUdO7QRCm3KYhtz4NrmvQSdfMMvmFHCRWDNSqB54U9Sd/KfdC3DkE64wQ1J2LDNU3RgJRTEjSc6OJAkRHqI+6WjKkU9kN57elsBjrfSgFwj9uIJT9fdEjHwpx76rk5Ml5bw3Ef/zOpHyLrox5WGkCMfpR17EoArgpCjYo4JgxcaaICyo3hXiARIIK11nTpdgzZ+8SJqVsnVWNm90G6cgRRYcgiNQBBY4B1VwDeqgATB4BM/gFbwZT8aL8W58pNGMMZvZB39gfP0AhJqitA==</latexit>
w = (w1, w2, . . . wN ) 2 RN
<latexit sha1_base64="wnXGk4kPKNgyQFFIyuWASwkJ91Q=">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</latexit>
x = (x1, x2, . . . xN ) 2 RN
generalized measure
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d⇤(w, x) =
N
X
i,j=1
(wi xi) ⇤ij (wj xj)
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d⇤(w, x) =
N
X
i,j=1
(wi xi) ⇤ij (wj xj)
relevance of a particular single feature
⇤ij contribution of a pair of features
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⇤ii
training: optimize prototypes and relevance matrix
w.r.t. performance on training data (objective function )
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n
w(k)
oK
k=1
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⇤
Generalized Matrix Relevance LVQ (GMLVQ)
3

5. Pregnenolone
17Preg
Progesterone
17OHP
Cholesterol
Deoxycortico-
sterone
11-
Deoxycortisol
Cortisol
18OH-Cortico-
sterone
Aldosterone
Cortico-
sterone
DHEA Androstenedione Testosterone DHT
HSD3B2
HSD3B2 HSD17B3 SRD5A2
HSD3B2
CYP21A2
CYP21A2
CYP17A1
CYP17A1
CYP17A1
CYP17A1
CYP11A1
CYP11B1
CYP11B2 CYP11B2 CYP11B2
Cortisone
HSD11B1
HSD11B2
Mineralocor*coids
Mineralocor*coid
precursors
Glucocor*coids
Androgens
Androgen precursors
Glucocor*coid
precursors
CYP11B1
steroidogenesis
adrenal
gland
www.ensat.org
possibly: +
hybrid steroids
(GC / MC)

6. urinary steroid metabolomics (USM)
gas chromatography-
mass spectrometry
(GC-MS)
Healthy Controls

7. USM for tumor classifica1on
adrenocortical tumors (adenoma vs. carcinoma)
benign ACA malignant ACC
features: e.g. 32 steroid metabolite excretion values
non-invasive measurement (24 hrs. urine)
steroid
#
set of
labelled
example
data
aim: develop a tool / support system for differential diagnosis
idea: analyse retrospective data by machine learning
identify characteristic steroid prototypes and relevances
www.ensat.org
6
2009

8. Generalized Matrix LVQ, ACC vs. ACA classification
o pre-processing: log-transformation of excretion values
• data split into 90% training, 10% validation set
• training: determine prototypes and relevance matrix
representative profiles (1 per class)
parameterizes distance measure
• validation: apply classifier to 10% hold-out data
evaluate expected performance (error rates, ROC, … )
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⇤ 2 R32⇥32
o repeat and average results over many random splits
tumor classifica1on
7

9. ROC characteristics
clear improvement due to
relevance learning
on average over 1000
randomized splits
1-specificity
sensitivity
diagonal rel.
Euclidean
full matrix
AUC
0.87
0.93
0.97
valida1on set performance
no relevances
only diagonal
full
insights beyond accuracy ?
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⇤ 2 R32⇥32
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⇤ii
8

10. … pairs of markers
(detailed inspection)
importance of single markers
insights: relevance matrix
5-PT 5-PD
THS
facilitates selection of reduced panels
with similar performance
relevances
9

11. ACA
ACC
relevances confirm – surprise – visualize
19 THS
individually
discriminative
10

12. relevances
(8) 5⍺ THA (12) TH-Doc
?
confirm - surprise - visualize
ACC
ACA
GMLVQ: multivariate analysis
discrimina+ve combina+on:
11

13. ACA
ACC
relevance matrix is dominated by leading eigenvectors
confirm – surprise - visualize
• visualize data set
and prototypes
q misclassifications?
• inspect individual cases
o uncertain cases
v outliers
12

14. excellent performance of USM + machine learning
suggest triple test strategy with excellent sensitivity and specificity
currently working on practical implementation in clinical practice
et al.
prospective
et al.
13
prospec1ve study
2020

15. 14
primary aldosteronism (PA)

16. 15
primary aldosteronism (PA)
PA - causes 5-10% of hypertension cases
- most frequent form of secondary hypertension
- increased risk for cardio- and cerebrovascular complica+ons
PA subtypes main treatment:
UPA (unilateral PA), one adrenal gland affected
by aldosterone producing adenoma (APA) surgery
several driver muta+ons in the tumor are known
BPA (bilateral PA) with both adrenal glands mineralocor6coid
over-producing, most frequently due to hyperplasia antagonists

17. 16
pa1ent cohort muta1ons (UPA)

18. 17
Heathy Control vs. all PA
near perfect
discrimina+on
of primary
aldosteronism
and controls
relevances

19. 18
PA related classifica1on problems
(similar results for RF)

20. 19
KCNJ5 vs. all other PA
very good
discrimina+on
of KCNJ5 type vs.
non-KCNJ5 PA
relevances

21. 20
main findings
- all PA vs. HC: excellent separa+on, characterized by increased excre+on
of mineralocortoid and glucocor+coid precursors
- all UPA vs. all BPA: subop+mal discrimina+on
- KCNJ5 vs. non-KCNJ5: very good discrimina+on (key: hybrid steroid 18-oxo-THF)
poten+al added value: KCNJ5-posi+ve cases are always unilateral
avoid invasive test (adrenal vein sampling)
improved therapy selec+on, KCNK5-posi+ve cases
respond beWer to treatment

22. 21
ongoing & future work on PA
more detailed relevance analysis
Iterated Relevance Matrix Analysis (IRMA)
S.S. Lövdal and M. Biehl, Proc. ESANN 2023
journal manuscript under review
improved classifiers
UPA vs. BPA, other subtypes of PA (?)
mul+-class problem wrt muta+ons (more data needed)
LC-MS instead of GC-MS
faster cheaper assessment of the steroid metabolome
also in other applica+ons of USM + machine learning

23. 22
other applica1on examples of steroid metabolomics
...

24. IEEE Members News, March 2021
exploit domain knowledge
(c) https://twitter.com/jessenleon
Let the data speak for itself
when the data cleans itself - unknown

25. 24
open access, 2023, 290 pages
University of Groningen Press
m.biehl@rug.nl
www.cs.rug.nl/~biehl