Aniket A. Tolpadi1,2, Jinhee J. Lee1, Valentina Pedoia1, and Sharmila Majumdar1
1Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, United States, 2Department of Bioengineering, University of California, Berkeley, Berkeley, CA, United States
Synopsis
Total
Knee Replacement (TKR) can relieve pain from osteoarthritis (OA), but patient
dissatisfaction is not uncommon, making TKR delay advisable until absolutely
necessary. Models could identify at-risk patients requiring nonsurgical
treatment, prolonging good health and delaying TKR. We present a pipeline that
uses DenseNet-121 to predict TKR onset from MRI images, integrates clinical
information by ensembling logistic regression models, and sensitively and
specifically predicts TKR, particularly at early-stage OA. Occlusion maps show
many OA progression imaging biomarkers are implicated in TKR, and many tissues
involved in knee flexion and extension preferentially affect TKR probability at
early-stage and late-stage OA, respectively.
Introduction
Knee Osteoarthritis (OA) is a common
musculoskeletal disorder in the United States that causes disability1,2.
Its progression is typically assessed with the Kellgren-Lawrence (KL) scale, a
0-4 scale in which higher scores indicate more advanced OA3. When
diagnosed early (KL=0,1), lifestyle alterations such as exercise and weight
loss can slow OA progression. At late stages (KL=4), no noninvasive option
exists, making total knee replacement (TKR) the only option4,5. The
procedure is effective but imperfect: only 66% of patients report knees feeling
“normal,” and 33% report pain post-implant6. Potential complications
can also necessitate revisions, making delaying TKR preferable whenever
possible7,8. Thus, a sensitive and specific model predicting if
patients will undergo TKR could identify patients in whom to initiative
nonsurgical alternative treatments, which would have clinical utility, particularly
for early-stage OA patients. Furthermore, if such a model utilizes medical
images to make predictions, it could identify imaging biomarkers for TKR.Methods
Data
was acquired from a prospective observational study conducted by the
Osteoarthritis Initiative (OAI)9. Posteroanterior radiographs were
cropped to a 500×500
region centered around the knee joint by a U-Net architecture, as done in (Norman,
2019)10. DESS MRIs were center-cropped to a 120×320×320 region. Both images
were then normalized and MRI pixels were rounded to nearest integers. Cases
were defined as patients who underwent a first TKR within 5 years (n=1,043);
controls were patients who did not undergo a TKR or who did so but the time to
it exceeded 5 years (n=34,441).
A DenseNet-121 classifier
was pretrained to predict OA from images and fine-tuned to predict TKR. Image-based
predictions were fed to an ensemble of logistic regression models based on OA
severity, yielding final predictions (Fig. 1). The ensembles were
optimized for Youden’s index11, integrating image-based predictions
with 20 non-imaging variables encompassing pain metrics, physical performance tests,
and demographics12-14. Performance of 2 ensemble versions are
reported: one where non-imaging variables were used to predict TKR (non-imaging
info. only), and one where image-based predictions were added (integrated model).
Sole DenseNet-121 output is also reported (image only). Confidence intervals of
accuracy, sensitivity, specificity, and AUCs were calculated by bootstrapping. Relative
performance of XRay and MRI pipelines were assessed within each OA
classification by calculating differences in AUCs on bootstrapped samples
(B=100) and running t-tests. Occlusion maps were developed for true positives,
and regions with pixels among the top 5% of TKR probability change designated
as hotspots.Results
AUCs
show that for integrated models, the MRI pipeline obtains significantly better
performance than its radiograph counterpart in patients without OA and overall,
p < 0.05 (Fig. 2a, 2b). The same holds for image-only models, p <
0.05 (Fig. 2c, 2d). ROC curves for three versions of the MRI pipeline are
displayed in Fig. 3.
An example occlusion map is
shown in Fig. 4, while Table 1 summarizes hotspot probabilities
in select tissues. Of tissues that were hotspots in over 75% of true positives,
the synovium, tibiofemoral joint (TFJ) cartilage
and bone (medial and lateral), anterior and posterior meniscus (medial and
lateral), Hoffa fat pad, and anterior cruciate ligament (ACL) are implicated in
OA progression15. The posterior cruciate ligament (PCL) and medial
patellar retinaculum (MPR) also had hotspot percentages above 75%; relevance of
PCL abnormalities to OA progression is unclear16,17, and that of the
MPR has not been thoroughly investigated. These tissues aside, others were
preferentially hotspots at no OA: the popliteal ligament and muscle,
semimembranosus tendon, gastrocnemius muscle and tendon, and lateral patellar
retinaculum. Many of these tissues are implicated in knee flexion or rotation. The
following preferentially affected TKR prediction at late-stage OA: patellofemoral joint bone and cartilage, quadriceps
tendon, patellar tendon, and suprapatellar fat pad. Many of these tissues are
implicated in knee extension.Discussion and Conclusions
We
present a sensitive and specific pipeline that predicts TKR, indicating
progress towards a clinically useful model. The MRI pipeline outperforms the
XRay pipeline, and MRI pipeline performance in particular at no OA—the most
difficult stage to model but most useful if modeled properly—is strong and exceeds
that of past work18. This all suggests MRI may have utility in TKR
screening despite high costs. Furthermore, this study performs a holistic analysis
for potential TKR imaging biomarkers. Its results are insightful, confirming
intuition that many OA progression biomarkers are implicated in TKR, and
showing tissues involved in knee flexion and rotation preferentially affect TKR
prediction at early-stage OA while those involved in knee extension do so at
late-stage OA. Past work has been inconclusive in determining whether weakness
or abnormalities in knee flexors or extensors can induce OA19,20,
but our findings suggest imaging features of these tissues may explain OA onset
and TKR where their strength may not. These findings justify future occlusion
map studies with finer voxel size, shorter stride, and case-control pairs to
confirm tissues as imaging biomarkers and investigate which features within
knee extensors, flexors, and other possible imaging biomarkers lead to TKR. Additional
directions include investigation of architectures other than DenseNet,
alternate means of integrating imaging predictions with non-imaging variables,
and utilizing images from multiple MRI sequences to improve performance.Acknowledgements
The OAI is a public-private partnership comprised of five
contracts (N01-AR-2-2258; N01-AR-2-2259; N01-AR-2-2260; N01-AR-2-2261;
N01-AR-2-2262) funded by the National Institutes of Health, a branch of the
Department of Health and Human Services, and conducted by the OAI Study
Investigators. Private funding partners include Merck Research Laboratories;
Novartis Pharmaceuticals Corporation, GlaxoSmithKline; and Pfizer, Inc. Private
sector funding for the OAI is managed by the Foundation for the National
Institutes of Health. This paper was prepared using an OAI public use data
set and does not necessarily reflect the opinions or views of the OAI
investigators, the NIH, or the private funding partners. This study was
supported by the grants R61AR073552 (S.M./V.P.) and R00AR070902 (V.P.).
Institutional research funds are provided by GE Healthcare for unrelated
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