- The document outlines the initial idea, team, target market size, and interviews conducted for a startup aiming to help oncologists select personalized breast cancer therapies using PET/X technology. - Over 10 weeks of interviews with 106 participants, the team validated the product-market fit, identified customer segments, and developed initial marketing and financial models. - Next steps include completing an MVP, partnering with clinical sites, collecting quality data, and submitting a Phase II proposal to further validate the technology and business models.

1. Team
17
PET/X
Improving outcomes, reducing costs
Initial idea: Help oncologists pick better personalized
therapies for breast-cancer patients
$10B
TAM
$1.2B
SAM
$600M
Target
Pa:ent
B
Tx
Failed
✗
Pa:ent
A
✓
Tx
Worked
Before
1
dose
Tx
AAer
Total
interviews:
106

2. Larry MacDonald
Co-­‐Founder
(IE)
Background:
Biomedical
Physics
Paul Kinahan
Co-­‐Founder
(CL)
Background:
Engineering
Physics
Team
17
William Hunter
Principal
Inves:gator
(PI)
Background:
Nuclear
&
Semiconductor
Physics
PET/X
Improving outcomes, reducing costs

3. Week
1:
Added
a
lot
to
explore
all
possible
VP,
CS,
and
RS

4. A"er
Week
1:
Posi.ve
indica.ons
for
key
VP
Hypothesis
Experiment
Results
New
technology
requires
trial
Asked
developers
of
previous
data
(mul:ple)
projects
A
magic
image
can
change
an
industry
more
quickly
(but
opposing
views
on
this)
Results
from
current
clinical
PET/CT
scanners
provide
low-­‐
fidelity
MVP
data
Asked
medical
imaging
clinical
experts
Affirmed
Insurance
companies
have
small
research
programs
Asked
ques:on
to
a
healthcare
economist
Some
insurance
companies
have
huge
research
programs/budgets
No
role
for
PET/X
in
diagnosis
Asked
mul:ple
clinicians
Strong
support
for
use
in
diagnosis
(opposing
views)
90%
of
new
BC
pa:ents
are
candidates
Asked
medical
imaging
clinical
experts
only
35%
for
sure,
could
go
up
to
75%
Payers
and
providers
are
separate
customers
Serendipitous
ques:on
to
a
healthcare
economist
Combined
payer/providers
would
be
good
first
adopters
No
reimbursement
limit
(on
numbers
of
scans)
Asked
na:onal
medical
imaging
clinical
experts
Affirmed

5. Week
4:
Start
to
rule
out
CS,
VP
based
on
interviews

6. W

7. eek
4
(VPCS):
Thinning

9. !
#

10. #
$%

11. '
(
(!
(

12. )!'
' *+
!!
,

13. ,
!-
,)
.
/,)
0
(,(
'1
'
2
Reimbursment
Decision
Maker
Coopera:ve
trial
PI

14. Aer
Week
4:
Differen.ated
Customer
segments
Hypothesis
Experiment
(n)
Results
Fixed
materials
price
:ers
;
materials
subject
to
tariffs
Interview
materials
vendor
(2)
Found
vendor
with
lower
and
(claimed)
controlled
price
Billing:
Reimbursement
is
feasible
Interviewed
billing
expert
(2)
Probably
yes,
but
there
are
Medicare
requirements
for
scanners
Radiologist/iCRO:
PETX
useful
for
clinical
trials?
Interviewed
radiologist
CTO
(1)
Could
be
(s:ll
gegng
mixed
results)
Interest
in
pre-­‐surgery
staging
Interviewed
surgeon
(2)
Confirmed
interest;
also
affirmed
poten:al
for
assessment
Easy
integra:on
into
clinical
workflow
Interviewed
clinical
manager
(2)
PETX
similar
to
other
add-­‐on
procedures
Guidance
for
balancing
resources
between
spinout
and
univ.
Interviewed
experienced
spin-­‐out
entrepreneur
(2)
Address
customers’
obstacles
to
adop:ng
PETX;
Confirmed
important
nego:a:ons
with
univ.

15. Week
10:
Validated
the
product
–
market
fit

16. Aer
Week
10:
More
Financial
Details
Hypothesis
Experiment
(n)
Results
ROI
in
5
years
Interviewed
Exec.
Directors
of
Radiology
(3)
For
mid-­‐range
capital
expenses
ROI
expected
in
~3
years,
but
varies
by
Org
size
Must
have
ROI
Price
Interviewed
Exec.
Directors
of
Radiology
(2)
Not
all
capital
equip
has
to
show
a
profit
if
it
brings
in
revenue
in
subsequent/other
services.
Technical
sales
staff
could
assist
with
supported
RD
Interviewed
Principal
Engineer
(1)
Tech.
sales
staff
(2)
Must
keep
separate
so
that
cost
of
clinical
studies
are
not
subtracted
from
system
cost
by
Medicare.
Margins
on
sales
of
capital
equipment
is
lower
than
small
device
margins
Interviewed
Tech.
sales
reps
(2
lrg
2
small
companies)
(4)
Hard
to
get
concrete
numbers,
but
confirmed
it
was
typically
less
than
65%.
Purchase
decision
based
solely
on
net
clinic
reimbursement.
Interviewed
Exec
Dir
Rad
(1)
Apending
Physicians
(4)
Breast
Cancer
is
an
emo:onally
charged
topic.
Huge
pa:ent
advocacy
pressure
can
drive
purchase
decision
even
at
net
loss.

17. PET/X
• Sales
• Service
Hospital
or
Clinic
using
PET/X
Pa:ent
Imaging
physician
CMS
Insurance
Capital
equipment
decision
influencers
Breast
cancer
advocacy
groups
Larger
Medical
Imaging
company
Capital
equipment
Decision
makers
Imaging
Technologist
Referring
Oncologist
NCCN
other
guidelines
Finances
opera:ons
revenue
stream
purchase
decision
influence
or
control
FDA
Crux
of
the
issue:
Purchase

18. Imaging
Site
Revenue
Model

19. PET/X
–
Sales
Revenue
Model
#
Pa:ents/year
Reimbursement
CPT
code
7811
Shared
Purchase
Fee
for
Service
3yr
Clinic
Purchase
ROI
No
4yr
ROI
No
5yr
ROI
Yes
Yes
Unlikely
to
buy
#
Scan
Opera:on
Cost
No
Yes
Direct
sell
price
Cost
of
Goods
Personnel
Sales
RD
Profit
GA
Training
Amor:zed
Cost
:me
Cost
of
Goods
Sales
RD
Profit
GA
Personnel
Training
Sales
RD
GA
Training
Profit
Cost
of
Goods
Personnel
Profit

20. Income,
Finance
and
Opera:ons
Timeline
Ac.vity/Item
Year
1
Year
2
Year
3
Scanner
Sales
2
4
8
Price
per
scanner
$650K
$650K
$650K
Service
Income
0
20%
of
prior
sales
20%
of
prior
sales
Total
Revenues
$1.30M
$2.86M
$5.98M
Cost
of
Goods
/
scanner
$195K
$203K
$211K
Personnel
/
scanner
$235K
$183K
$143K
Sales
/
scanner
$50K
$40K
$30K
Training
/
scanner
$50K
$40K
$35K
RD
0
5%
of
Y1
revenue
5%
of
Y2
revenue
GA
$200K
$220K
$242K
Total
Expenses
$1.26M
$2.23M
$3.89M
Net
$40K
$633K
$2.09M
Cumula.ve
Net
$40K
$673K
$2.76M

21. Medical
Device
Investment
Readiness
Level
Plausible
exit
Cash
to
exit
Unit
economics
Validated
Reimbursement
Regulatory
Intellectual
Property
Aprac:ve
solu:on
ID
of
MVP
Compelling
clinical
need
+
large
mkt
Effec:ve
team?
3.5
4.5
IRL
7
IRL
6
Oct
10
Dec
10
discovered
data

22. Road
to
first
sale
We
now
have
• Defined
customer
sub-­‐segment:
therapy
imaging
centers
for
invasive
breast
cancer
• Ini:al
marke:ng
informa:on
financial
models
• Validated
preferred
exit
(licensing)
• Submiped
a
Phase
II
using
materials
from
I-­‐Corps
on
valida:on
of
product-­‐market
fit
and
financial
models
Next
we
will
• Complete
MVP
• Partner
with
luminary
sites
• Collect
disseminate
quality
data

24. !
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Week
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181. Week
10
Drilling
down
on
details

182. Customer
types
Interviewed
Type
(subtype)
n
Medical
Oncologist
6
Surgeons
3
Imaging
Physicians
23
Nuc
Med
Imaging
12
Mammo
Imaging
4
Diagnos;c
Imaging
3
Rheumatology
2
Technologists
6
mammo
4
nuc
med
2
Pharma
4
RD
2
Exec
2
Type
(subtype)
n
Radiology
/
Hospital
5
business
execu:ves
Regulatory
5
Reimbursement/coding
1
IP
Lawyer
1
Health
Economist
1
Imaging
Scien:st
7
Sta:s:cian
1
Clinical
Physicist
10
Imaging
industry
33
Sales/marke;ng
9
rd,
CTO
9
execu;ves
12
compe;ng
3