# Deep learning can accurately predict the prognosis of gynecologic smooth muscle tumors of uncertain malignant potential: a multicenter pilot study

João Costa, Van-Linh Le, Antonio de Leo, Caterina Ravaioli, Valérie Velasco, Ben Davidson, Tone Skeie-Jensen, Mojgan Devouassoux-Shisheboran, Alexis Trecourt, Carla Bartosch, et al.

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## ► To cite this version:

João Costa, Van-Linh Le, Antonio de Leo, Caterina Ravaioli, Valérie Velasco, et al. Deep learning can accurately predict the prognosis of gynecologic smooth muscle tumors of uncertain malignant potential: a multicenter pilot study. Laboratory Investigation, 2025, 105 (10), pp.104211. 10.1016/j.labinv.2025.104211. hal-05217465

HAL Id: hal-05217465

https://inria.hal.science/hal-05217465v1

Submitted on 21 Aug 2025

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# Deep learning can accurately predict the prognosis of gynecologic smooth muscle tumors of uncertain malignant potential: a multicenter pilot study

## Short running title: Outcome prediction in STUMP using deep learning

List of contributors:

João Costa¹*, Van-Linh Le²,³,⁴,⁵*, Antonio De Leo⁶, Caterina Ravaioli⁷, Valérie Velasco⁸, Ben Davidson⁹, Tone Skeie-Jensen¹⁰,¹¹, Mojgan Devouassoux-Shisheboran¹²,¹³, Alexis Trecourt¹²,¹³, Carla Bartosch¹⁴, Elisabete Rios¹⁵, Catherine Genestie¹⁶, Patricia Pautier¹⁷, Coriolan Lebreton¹⁸, Frédéric Guyon¹⁹, Guillaume Babin¹⁹, Jean-Michel Coindre⁸, Francois Le Loarer⁸,²⁰,²¹, Olivier Saut²,³†, Sabrina Croce⁸,²,²¹†

1. UNILABS - LAP: Laboratório de Anatomia Patológica, Porto, Portugal.

2. MONC team, INRIA Bordeaux Sud-Ouest, Talence, France.

3. Bordeaux Mathematics Institute UMR 5251 (IMB), University of Bordeaux, CNRS and Bordeaux INP, Talence, France.

4. Bordeaux Institute of Oncology, BRIC U1312, INSERM, University of Bordeaux, Institute Bergonié, Bordeaux, France.

5. Department of Data and Digital Health, Institute Bergonié, Bordeaux, France.

6. Solid Tumor Molecular Pathology Laboratory, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.

7. Pathology Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.

8. Department of Biopathology, Institute Bergonié, Comprehensive Cancer Centre, Bordeaux, France.
9 - Department of Pathology, Oslo University Hospital, Norwegian Radium Hospital; Faculty of Medicine, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.

10 - Department of Gynecologic Oncology, Oslo University Hospital, Norwegian Radium Hospital, Oslo, Norway.

11 - Haukeland University Hospital, Bergen, Norway.

12 - Department of Pathology, CHU Lyon Sud, Hospices Civils de Lyon, Pierrebenite, France.

13 - Université Claude Bernard Lyon 1, Centre pour l'innovation en Cancérologie de Lyon, UR3738, Lyon, France.

14 - Department of Pathology, Instituto Português de Oncologia do Porto, Francisco Gentil EPE, Porto, Portugal.

15 - Department of Pathology, Unidade Local de Saúde São João EPE, Porto, Portugal

16 - Department of Pathology, Gustave-Roussy Cancer Campus, Villejuif, France.

17 - Department of Medicine, Gustave-Roussy Cancer Campus, Villejuif, France.

18 - Department of Oncology, Institute Bergonié, Comprehensive Cancer Centre, Bordeaux, France.

19 - Department of Surgery, Institute Bergonié, Comprehensive Cancer Centre, Bordeaux, France.

20 - Université de Bordeaux, Talence, France.

21 - Inserm U1312, Université de Bordeaux, Bordeaux, France.

*Contributed equally and share first authorship

†Contributed equally and share senior authorship

Corresponding Author:

- Name: Sabrina Croce

- ORCID ID: 0000-0002-6487-5418

- Address: Department of Biopathology, Institute Bergonié, Comprehensive Cancer Center, 229 Cours de l'Argonne 33076 Bordeaux - France

- Telephone number: + 33 0 (5) 56 33 33 33

- E-mail: s.croce@bordeaux.unicancer.fr
Conflict of Interest: The authors do not have any financial or non-financial interests that are directly or indirectly related to the work submitted for publication.

**Word count: 3517 / 7000**
## Abstract

Smooth muscle tumors of uncertain malignant potential of the gynecologic tract (STUMP) are a heterogeneous group of tumors, with ambiguous or worrisome features, whose biological behavior is difficult to predict. Several ancillary techniques have been used to try to predict their prognosis, with limited success. The aim of this study is to explore whether deep learning (DL) based features can be used to predict progression-free survival (PFS) in STUMP and identify high-risk patients, directly from histological slides. A cohort of 95 STUMP was collected from 7 academic centers (79 for training and 16 for external validation). Non overlapping tiles were extracted from the tumor area and used to train a DL model to predict PFS. Python's scikit-learn library and the R software environment were used for data analysis. After 4-fold cross-validation, mean C-indexes of 0.7052 (95%CI: 0.4951-0.9152) and 1.0 (95%CI: 1.0-1.0) were achieved, in the training and external validation cohorts, respectively. The predicted PFS probabilities were used to classify the patients into low-risk and high-risk groups, based on the thresholds of the median and the first quartile of predicted PFS probabilities. Significant differences between both groups were observed, at 10 years, with both thresholds. Cox regression analysis showed that the output of the DL model was associated with a worse prognosis (p = 0.0356). Both STUMP groups were compared with a cohort of leiomyomas (n = 160) and leiomyosarcomas (n = 58). The lowest hazard ratio was observed in leiomyomas, followed, consecutively, by low-risk STUMP, high-risk STUMP and leiomyosarcomas. The Cox model showed good discriminatory potential between the four groups (all pairwise comparisons were statistically significant). These findings suggest that DL-based features can be used for outcome prediction of STUMP. Additional work is needed to establish whether this "high-risk" group can be identified via molecular markers and used to tailor patient surveillance.

## Keywords

Smooth muscle tumors of uncertain malignant potential, Deep learning, Gynecologic smooth muscle tumors, Prognosis prediction, STUMP
Abbreviations

CI: Confidence interval.

DL: Deep learning.

F: Fully connected.

H&E: Hematoxylin and eosin.

HES: Hematoxylin, eosin, and natural saffron.

HR: Hazard ratio.

PFS: Progression-free survival

STUMP: Smooth muscle tumor of uncertain malignant potential.

WSI: Whole slide imaging.
Introduction

Gynecologic smooth muscle tumors are broadly divided into leiomyomas and leiomyosarcomas, based on a combination of mitotic activity, tumor cell necrosis and cytologic atypia [1-3]. However, a small number of problematic lesions may present morphological features of challenging interpretation, introducing a degree of subjectivity and poor reproducibility in the diagnosis [2]. Problematic lesions of difficult classification have been designated as smooth muscle tumors of uncertain malignant potential (STUMP), a term first coined by Kempson in 1973 [4]. STUMP are a heterogenous group of smooth muscle tumors presenting worrisome or ambiguous morphological features that preclude classification as unequivocally benign or malignant [2, 5-7]. STUMP have an inherently unpredictable behavior and their clinical course is what ultimately defines whether a specific lesion behaves more akin to a leiomyoma or to a leiomyosarcoma. Improving the prediction of the behavior of STUMP and decreasing the number of cases classified as such is of significant importance for clinical management.

Several ancillary techniques have been studied as a method for complementing the morphological analysis. Immunohistochemical panels using p16, Ki67, p53, Bcl-2, ATRX, DAXX, among others, have been used with limited success [8-13]. Array comparative genomic hybridization has been shown to divide STUMP two groups: one with a risk of recurrence/metastasis and one without risk of recurrence/metastasis [14-16]. However, the reliability of this technique is affected by problems of reproducibility between different platforms and the fact that some variants of leiomyoma (such as leiomyomas with bizarre nuclei), present some level of genomic complexity [17]. Methylation data appears to be promising, although only a small number of cases have been studied [18].

In recent years, with increased availability of whole slide imaging (WSI) data, the interest in deep learning image analysis methods, to detect and extract relevant features, has exploded in the different fields of pathology. Most of the previous works have targeted methods for segmentation and classification of objects and regions in tissue images. A smaller but growing field has focused in developing models that directly learn the morphological features associated with
survival, molecular characteristics and treatment response. Several deep
learning models have been developed in recent years to recognize
histopathological features in sarcomas associated with clinical, molecular and
prognostic features [19-23]. These methods could hold promise for improved risk-
stratification in STUMP.

The aim of this study is to develop a deep-learning model, based on
features extracted from WSI, to predict progression-free survival (PFS) of
STUMP.
# Materials and methods

## Case collection

The 79 cases used for training the model were collected from the archives of the pathology departments from the Institute Bergonié (37 cases), Instituto Português de Oncologia do Porto Francisco Gentil EPE (5 cases), Unidade Local de Saúde São João EPE (3 cases), Gustave Roussy Cancer Center (12 cases), Hôpital Lyon Sud (4 cases) and the Oslo University Hospital/The Norwegian Radium Hospital (18 cases). The 16 cases used as an external validation cohort were collected from IRCCS Azienda Ospedaliero-Universitaria di Bologna (14 cases) and from the Institute Bergonié (2 additional cases, received for consultation after building the training cohort). The cases were selected based on the availability of histopathological material of the primary tumor (regardless of the specimen type) and the availability of follow-up data. There were no exclusion criteria regarding the amount of viable tumor. The pathology reports and clinical records were retrospectively reviewed to collect all relevant clinical and pathological data for each case.

Additionally, a cohort of 160 leiomyomas and 58 leiomyosarcomas were also retrieved from the archives of the pathology department at the Institute Bergonié, with the aim of comparing the results obtained within the spectrum of gynecological smooth muscle tumors.

## Material preprocessing

Hematoxylin and eosin (H&E) and hematoxylin, eosin, and natural saffron (HES) stained slides were collected then digitalized for WSI using Hamamatsu NanoZoomer® S360 (Hamamatsu, Japan) and a VENTANA® DP 600 (Roche Diagnostics GmbH, Austria) (the last one was only used for scanning the slides from IRCCS Azienda Ospedaliero-Universitaria di Bologna), at a resolution equivalent to a 40X microscope objective. All cases were reviewed by SC and JC and classified according to the diagnostic criteria for STUMP outlined in the 5th Edition of the WHO Classification of Tumors of the Female Genital Tract [24]. The reviewers classified the cases based solely on the morphologic characteristics and were blinded for the clinical course of the patients. For each STUMP case, a
single representative slide was selected and the area with the tumor outlined by the pathologists. Non-overlapping tiles were extracted from the tumor area in the WSI. In the tile extraction, a process based on the OpenSlide package (version 2.1) was used to get pixel array from the WSI and tumor area annotated. The number of extracted tiles varied among patients, depending on the size of the annotated areas, with a mean tile count of 50,000 per patient.

A pre-trained deep learning model, ResNet50 [25], was utilized to extract high-dimensional features from the tiles derived from WSI. The extracted features represent a rich set of spatial and semantic information, morphological patterns and prognostic information, were stored in a matrix of size N×2048 (where N corresponds to the total number of tiles extracted from the WSI). This matrix served as the input for model training in order to predict the PFS of the patients.

## Deep learning model

Figure 1 presents the deep learning model used to predict the PFS of the STUMP. The system included two modules: a first module (named Deep-Attention module) for feature aggregation, and a second module, for prediction of PFS. The Deep-Attention module is composed by the layers before the Attention unit [25], responsible for aggregating the features of each tile, and the Attention unit, which provides an attention score for each tile. These scores present the importance of each tile for the prediction of the layers in the objective modules. The input of this module is a matrix of (N × 2048) described above. The module begins with a 1D convolutional layer (Conv1D), which captures local dependencies and patterns within the extracted histopathological features, followed by a Parametric ReLU (PReLU) activation function to introduce non-linearity while allowing adaptive negative slopes. A Dropout layer follows to prevent overfitting by randomly deactivating some neurons during training. The output is then passed through a fully connected (FC) layer with PReLU activation to refine feature representation, followed by another Dropout layer for regularization. The processed features enter the attention module, which consists of two fully connected layers: the first applies a Tanh activation function, enabling it to learn both positive and negative feature importance, while the second applies a Softmax activation, generating attention scores that highlight the most relevant regions of the histopathological data. At the end of this module, the attention
score of each is multiplied by the corresponding features and these features are
merged to obtain a combined feature presented at the patient level, which will be
used as the input for the next modules.

The objective module, named Survival Prediction Module, consists of three
fully connected (FC) layers designed to estimate survival probabilities. The first
two FC layers use Parametric ReLU (PreLU) activation, allowing adaptive non-
linearity to enhance feature learning. These layers help capture complex patterns
relevant to survival prediction. The final FC layer applies a Sigmoid activation
function, transforming the learned features into probability values between 0 and
1. This ensures that the model produces interpretable survival probabilities over
the time, for each patient.

Statistical analysis was performed using Python's scikit-learn library
(version 1.0.2) and the R software environment (version 4.4.0). Results were
considered statistically significant when the p-value was less than 0.05.
# Results

## Clinical characteristics

A total of 95 STUMP were collected (figure 2). The characteristics of the training and external validation cohorts are outlined in table 1.

## Performance evaluation of the deep-learning model

The deep learning model was trained on a 4-fold cross validation, with different sets for training and validation: the overall mean C-index was 0.7052 [95% confidence interval (CI): 0.4951-0.9152] in the training cohort and 1.0 (95%CI: 1.0-1.0) in the external validation cohort.

## Multivariable analysis of the deep learning model data and pathological data

A multivariable Cox proportional-hazards regression model was built to evaluate the significance of the deep learning model data as a predictor of PFS, after adjusting for several pathological variables (mitotic count per mm², type of atypia, presence of tumor cell necrosis and presence of vascular intrusion). Using only the four pathological variables for the Cox model, a mean C-index of 0.726 (95%CI: 0.5868-0.8652; p=0.005) was obtained. Using the output of the deep learning model and the four pathological variables for the Cox model, a mean C-index of 0.766 (95%CI: 0.6308-0.9012; p=0.002) was obtained. The performance of the Cox model appeared to be slightly improved by adding the output of the deep learning model. In the Cox model with the four pathological variables, only the mitotic count was significantly associated with a worse outcome [Hazard ratio (HR) = 1.73, 95%CI: 1.05-2.9; p=0.0321]. In the Cox model with the four pathological variables and the output of the deep learning model, only the mitotic count (HR = 1.86, 95%CI: 1.11-3.13; p=0.0193) and the output of the deep learning model (HR = 3.15, 95%CI: 1.08-9.16; p=0.0356) were significantly associated with a worse prognosis. In another analysis to investigate which single variable is the best predictor of PFS, we fixed the Cox model to each variable, individually, and recorded the C-index: the mitotic count and the output of the deep learning model were the best-performing variables, with the C-indices of
0.6676 (p = 0.009) and 0.6808 (p = 0.0204) respectively, whereas the other variables achieved a C-index less than 0.6. It was not possible to perform Cox proportional hazards analysis in the external validation cohort due to a failure in model convergence. This lack of convergence is due to the limited sample size and the low event rate in the external validation cohort (2 recurrences among the 16 cases), which are insufficient to reliably estimate hazard ratios, rendering the application of the Cox model, in this context, statistically inappropriate.

## Comparing the survival rates in low and high-risk groups

The output of the model is a continuous risk score that can be used as a feature for survival analysis. In order to define low and high-risk groups, two different thresholds were used: the median of predicted PFS probabilities and the 1st quartile of predicted PFS probabilities (only the 1st quartile was selected due to the fact that the predicted PFS were high and the differences versus the other quartiles were very small). The survival curves of two groups (low-risk and high-risk of recurrence) are displayed in figure 3. Using the threshold of the median predicted PFS probabilities, overall, there were no differences between the groups throughout the entire follow up period (p=0.0588), but significant differences in predicted PFS at 10 years of follow-up, between the groups were observed (p=0.0248). Using the threshold of the 1st quartile of predicted PFS probabilities, overall, significant differences were observed between the groups throughout the entire follow up period (p=0.0001) and at 10 years of follow-up, (p=0.0147). No significant differences were observed between groups in the predicted PFS probabilities at 5 years, using both the median threshold (p=0.3963) and the 1st quartile threshold (p=0.6831). Using the 1st quartile as a threshold appeared to lead to an improvement in risk prediction, comparing to using the median. Significant differences between both groups were also observed, in the testing cohort, using the median threshold (p < 0.001) (figure 3), but not using the 1st quartile threshold (all cases were classified as low-risk using this threshold).
## Evaluation of the histomorphological features most relevant for the model

To characterize the histomorphological features most relevant to the model and potentially associated with worse prognosis, class activation maps were generated to highlight specific image regions with the highest prediction probability for each patient. A heat map overlaid on the original image was generated, to visualize these regions, and the 25 tiles with the highest prediction probabilities were extracted for detailed analysis by two pathologists (SC and JC) [26]. Despite a systematic evaluation of these tiles, no consistent or salient, recurrent morphological features were identified in tumors of the high-risk group or in tumors of the low-risk group that recurred. The selected tiles represented areas with variable cellularity and atypia of the tumors, with a variable proportion of the stromal component (figure 4).

## Comparison with a cohort of leiomyosarcomas and leiomyomas

In order to understand how the low-risk and high-risk groups of STUMP compared with other entities in the spectrum of gynecologic smooth muscle tumors, the two groups were compared with a cohort of leiomyomas and leiomyosarcomas, using a Cox model. The hazard ratios and survival curves of the model, using the low-risk/high-risk groups defined by the median threshold of survival and by the 1st quartile threshold, are outlined in figures 5 and 6, respectively. For each threshold, as expected, the lowest risk was observed in leiomyomas, followed, consecutively, by low-risk STUMP, high-risk STUMP and leiomyosarcomas. The Cox model showed good discriminatory potential between the four different groups, as shown by the survival curves (all pairwise comparisons were statistically significant, presented in supplementary figures 1 and 2).
Discussion

The advent of WSI scanning and the concomitant rise in deep learning
models for assessing digital images of slides have resulted in a massive increase
in interest in the use of artificial intelligence technologies in digital pathology. The
prediction of the clinical behavior of STUMP remains a significant clinical
challenge. This study proposes a deep-learning model to implement survival
analysis in STUMP patients. The model can successfully stratify patients in low-
risk and high-risk groups and the output of the model is an independent
prognostic factor. The model allows significant discrimination of low-risk and high-
risk STUMP within the biologic spectrum of gynecologic smooth muscle tumors.

The clinical and pathological characteristics of our cohort are similar to
other STUMP cohorts published in the literature. The overall recurrence rate was
of 21,1%, which is in line with the values reported in literature, between 7,3-36,4%
[5, 27-29]. As stated previously, all cases were reviewed and classified in
accordance with the current WHO guidelines, with the reviewers blinded for
follow-up data, to ensure reproducibility. A number of cases presented atypical
features, not stated in the WHO criteria: 5 cases presented infiltrative/irregular
borders and 3 cases presented vascular intrusion. These cases were maintained,
based on previous reports of STUMP bearing these characteristics [5].

The performance of the deep learning model in the external validation
cohort presented a C-index of 1.0. Whilst this result appears to translate a perfect
performance, the external validation cohort only had 16 cases, which raises
questions regarding the validity of this metric in this particular setting. However,
there are several points arguing in favor of a strong predictive performance and
generalizability of the model. The cases with no recurrences in the external
validation cohort presented the highest predicted PFS probabilities and the cases
with recurrences presented the lowest predicted PFS probabilities. In addition,
significant differences were observed between low-risk and high-risk groups
using median threshold. These arguments underline the model's value, not only
in the binary classification, but also regarding the usefulness of individual risk
scores in guiding patient management.
The model presented in this study is a weakly supervised model (only the tumor area was annotated). Given the fact STUMP are classified on the basis of the cytologic atypia, tumor cell necrosis and mitosis, a natural question arises regarding the value of developing algorithms to predict prognosis exclusively from the output of the deep learning model, versus leveraging region-level annotations for known pathological features of interest. Weak supervision avoids the cost, tediousness, and difficulties associated with feature and cellular-level annotations. In addition, weakly supervised models have a relatively unbiased nature, with the potential of learning previously unknown or unappreciated features. On the other hand, weak supervision usually requires a larger number of cases to train accurate models. This problem is compounded by the rarity of STUMP, which makes collecting a cohort large enough for training deep learning models a challenge. Future work to better understand the effects of sample size, as well as the effects of image and disease-specific features on clinical predictions from WSI will be important in this field.

In this study, the Cox model combining the deep learning output and the pathological variables appeared to predict the PFS better than the model with only pathological variables. This might be due to the fact that the features extracted from the WSI could provide additional details regarding the tumor microenvironment and other unappreciated features, which might be associated with underlying molecular pathways and allow a more comprehensive prediction of prognosis. In an effort to understand whether the learned features could be used to supplement current practices in histopathological analysis of STUMP, the most prognostically meaningful tiles were reviewed. No salient and/or recurrent histological features were found – these tiles presented areas with variable cellularity and atypia, with variable proportions of stroma. Some studies have found that stromal elements may have prognostic significance, but, based on our findings, this does not appear to be the case in STUMP [30, 31]. Elucidating whether the morphological features obtained from the deep learning model can help distinguish between low-risk and high-risk cases remains an exciting but challenging topic for future efforts. Morphometrical analysis might help in further characterizing the features in the tiles with the highest predictive power.
Several studies have used features extracted from WSI for survival prediction in sarcomas [19, 20, 23, 32]. There is also a small, but growing body of literature regarding the use of deep learning in gynecologic mesenchymal tumors, mostly for classification tasks. Yu et al. developed a deep learning model for classifying leiomyosarcomas, leiomyomas and STUMP, combining the output of automated detection algorithms for atypia, necrosis and mitosis [33]. This model achieved a precision of 0.90 for the classification of the different histopathological types. Zehra et al. developed an artificial intelligence-based approach for mitosis detection in a dataset of uterine leiomyosarcomas, achieving a precision of 0.7462, a recall of 0.8981, and a F1-score of 0.8151 [34]. Regarding the use of deep learning from WSI for survival prediction in gynecologic cancer, most studies have focused on cervical cancer [35] and ovarian cancer [36-39]. To our knowledge, so far, no studies have targeted survival prediction specifically in gynecological mesenchymal tumors.

To the best of our knowledge, this is the first study to apply deep learning for prognosis prediction of STUMP. The primary advantage of deep learning models over traditional statistical methods is ability to detect nonlinear relationships between model inputs (WSI features) and model outputs (survival data). This study gathered the largest cohort of STUMP ever reported in literature, from several institutions. The use of H&E and HES slides from different institutions, performed at different points in time and scanned using different scanners raises the credibility of the model, due to the inter-institutional variability across time and technical procedures.

There are several limitations to our study. First, the small number of cases used for training and external validation. Given the rarity of STUMP, a total of 95 cases (79 for training and 16 for external validation) were gathered, resulting in wide confidence intervals that limit statistical conclusions (highlighting the importance of reporting confidence intervals as one of the key model robustness indicators in studies of this kind). The use of the largest, multi-institutional cohort of STUMP ever published should improve generalizability. These findings require further validation in larger datasets, to improve prediction and the accuracy of the estimation of effect sizes, with a goal to demonstrate practical clinical value. The retrospective nature of the study and the fact that most cases were drawn from a
consultation/referral basis represents an unavoidable source of selection bias
and data inhomogeneity. The choice to use a single slide was made due to
storage constraints, the presence of a single label per case (PFS) and the type
of architecture used for the model (multiple instance learning). It is unlikely this
choice had a material effect in the deep learning model (all slides were chosen
based on the representativity of the histopathological features used for the
STUMP criteria). Future work using additional slides is necessary to understand
the impact of using one or more slides in prognostic stratification with these types
of DL model architectures. The possible confounding effect of treatment
differences between patients is not an issue, since all patients were followed after
surgery without adjuvant treatment and, to our knowledge, none of the patients
were submitted to hormonal treatment before surgery. This study did not
incorporate available molecular information from array comparative genomic
hybridization and transcriptomic profile, since this data was only available for a
small number of cases from one center.

The findings in this study suggest that deep learning-based features can
be used to predict the outcome of STUMP. This could substantially improve the
clinical management of the patients with STUMP. Further work is needed to
validate these findings in a larger cohort and to ascertain whether this "high-risk"
group can be identified via morphological features and molecular markers, with
the goal of tailoring patient treatment and surveillance.
**Ethics Approval/ Consent to participate**

The research was conducted ethically in accordance with the Declaration of Helsinki. The study's design was approved by the Institute Bergonié's institutional review board (IB 2023-STUMP) and waived the informed consent requirement.

**Author contribution statement**

All authors contributed to the study conception and design. Review and selection of tumor slides by JC, SC and JMC. LVL and OS constructed the deep learning model and performed statistical analysis. JC, SC, JMC, PP, TS, AT, MD, CL, FG and GB contributed to gathering follow-up data. VV provided histotechnical support. The study was elaborated by JC, LVL, FLL, OS and SC. BD, CG, MD, AT, AL, CR, JC, SC, JMC, CB and ER contributed with slides and samples to the series. Literature review and analysis were performed by JC, SC and LVL. The first draft of the manuscript was written by JC and SC and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

**Funding statement**

The authors did not receive any research funding to support this study.

**Data availability statement**

The data used in this study contains sensitive patient information and is therefore subject to confidentiality and data protection regulations. As such, it cannot be made publicly available. However, access to the data may be granted upon reasonable request, contingent on approval by the relevant institutional ethics committees and the establishment of appropriate data-sharing agreements to ensure compliance with all applicable privacy regulations.
## Figure legends

**Figure 1:** The overview of the architecture of the deep learning model.

**Figure 2:** Different types of smooth muscle tumors of uncertain malignant potential (STUMP) included. Pictures taken at a resolution equivalent to a 20X microscope objective. All mitotic counts stated considered a field with 0.55 mm in diameter and 0.238 mm² in area. A: Epithelioid STUMP. | B: Spindle STUMP lacking cytological atypia and tumor cell necrosis, but with ≥ 6.3 mitoses/mm². | C: Spindle STUMP with diffuse nuclear atypia. | D: Spindle STUMP with focal/multifocal or diffuse nuclear atypia, with 2–3.8 mitoses/mm². | E and F: Spindle STUMP with necrosis of uncertain type/tumor cell necrosis.

**Figure 3:** The survival curves of low-risk and high-risk groups of smooth muscle tumors of uncertain malignant potential (STUMP), in the training cohort (top and middle rows) and in the external validation cohort (bottom row). Top row: Groups defined by the threshold of the median of the predicted progression-free survival (PFS) probabilities. Middle row: Groups defined by the threshold of the 1st quartile of the predicted PFS probabilities. Bottom row: Groups defined by the threshold of the median of the predicted PFS probabilities. From left to right: Follow-up time of the entire cohort, 5 years of follow-up, and 10 years of follow-up.

**Figure 4:** Tiles with the highest prediction probability, from different cases. Top row: Tiles from cases classified as low-risk, using the median and the 1st quartile of predicted progression-free survival (PFS) probabilities. | Middle row: Tiles from cases classified as high-risk, using the median and the 1st quartile of predicted PFS probabilities. | Bottom row: Tiles from cases classified as low-risk which presented recurrences, using the median and the 1st quartile of predicted PFS probabilities.

**Figure 5:** Comparison of the low-risk and high-risk groups of smooth muscle tumors of uncertain malignant potential (STUMP) with a cohort of leiomyomas (LM) and leiomyosarcomas (LMS). The low-risk and high-risk groups were defined based on the median of the predicted progression-free survival (PFS). | A: Relationship between the different groups regarding the predicted PFS. LMS were used as a reference. The 95% confidence interval (CI) in LM varies between 0 and infinite due to the lack of events in this group. HR – Hazard ratio. | B:
Comparison of the progression-free survival curves across the entire follow-up time of the cohort. | C: Comparison of the progression-free survival curves at 10 years of follow-up.

**Figure 6:** Comparison of the low-risk and high-risk groups of smooth muscle tumors of uncertain malignant potential (STUMP) with a cohort of leiomyomas (LM) and leiomyosarcomas (LMS). The low-risk and high-risk groups were defined based on the threshold of the 1st quartile of the predicted progression-free survival (PFS). | A: Relationship between the different groups regarding the predicted PFS. LMS were used as a reference. The 95% confidence interval (CI) in LM varies between 0 and infinite due to the lack of events in this group. HR – Hazard ratio. | B: Comparison of the progression-free survival curves across the entire follow-up time of the cohort. | C: Comparison of the progression-free survival curves at 10 years of follow-up.

**Table 1:** Summary of clinical and pathological data of the smooth muscle tumors of uncertain malignant potential (STUMP), used as a training cohort (A) and as an external validation cohort (B). All data have been rounded up to the nearest decimal. All mitotic counts stated considered a field with 0.55 mm in diameter and 0.238 mm² in area. Next to the title of each variable, the total number of cases for which data is available is listed. | 1 Data in “Age”, “Size”, “Time to recurrence” and “Follow-up time” is presented as median (minimum, maximum). | 2 The cases of core-needle biopsy were diagnosed as STUMP in the surgical specimens, but no histopathological material of the surgical specimens was available for scanning. | 3 Six patients died of disease: two with epithelioid STUMP, one with a spindle STUMP with focal/multifocal or diffuse nuclear atypia, with 2–3.8 mitoses/mm², one with a spindle STUMP with diffuse nuclear atypia, one with a spindle STUMP with necrosis of uncertain type/tumor cell necrosis and one with a spindle STUMP lacking cytological atypia and tumor cell necrosis, but with ≥ 6.3 mitoses/mm². Three of the patients presented distant metastasis at recurrence, two patients presented locoregional recurrences and in one patient, the location of the recurrence was unknown. The median time to recurrence for these patients (n = 5; in one of the patients, the date of recurrence was unknown) was 15 (1; 35) months. The median follow-up time (n = 6) was 91.5 (16; 186)
months. | 4 The date of recurrence was unknown for one of the patients that recurred.
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Acknowledgements

This work is supported by SIRIC Bordeaux and Fondation Bergonié. This study
is carried out in the framework of a collaboration between MONC team, Center
INRIA at the University of Bordeaux, the Sarcotarget team – BRIC, and Institute
Bergonié.