Introducing UroCCR
The goal of the French Kidney Cancer Research Network (Réseau Français de Recherche sur le Cancer du Rein) is to connect a National, multidisciplinary network of medical and scientific professionals who focus on therapeutic management and applied research into kidney cancer.
Today, the UroCCR network includes 53 multidisciplinary clinical teams across France and Belgium. It is labelled by the French national Cancer Institute (INCa) and referenced by the High Authority of Health (HAS) as a valuable source of real-world data.
One of the world’s largest collaborative kidney cancer databases
All clinical, biological, and radiological data from newly diagnosed kidney cancer patients in the UroCCR network are collected in a National, multidisciplinary, clinical and biological database which is shared and used to power collaborative research projects.
JUST PUBLISHED!
UroPredict: Machine learning model on real-world data for prediction of kidney cancer recurrence (UroCCR-120)
Try the RCC* recurrence predictor
For research use only – Not for use in diagnostic procedures.
*Renal Cell Carcinoma
Harnessing machine learning to advance kidney cancer research
UroCCR and SOPHiA GENETICS joined forces to develop machine learning models: First to predict prior to surgery whether kidney cancer would upstage, and second to predict whether kidney cancer would recur after surgery.
Research objectives
Preoperative prediction of pT3a upstaging in localized or locally advanced RCC
To develop a machine learning-based, contemporary, clinically relevant model for pre-operative prediction of renal cell carcinoma pT3a upstaging in patients undergoing nephrectomy for cT1/cT2a renal cell carcinoma.
In simple terms: Develop a model that can predict whether kidney cancer will progress from a localized tumor to a locally advanced tumor (associated with worse prognosis) before the patient undergoes surgery.
Prediction of prognosis after surgery in localized or locally advanced RCC
To develop a machine learning-based, contemporary, clinically relevant model for prediction of disease-free survival in patients undergoing surgery for localized or locally advanced renal cell carcinoma.
In simple terms: Develop a model that can predict whether a patient’s kidney cancer will recur after they undergo surgery.
CHU de Bordeaux
CHU de Toulouse
CHU de Lyon
CHU de Rennes
CHU d’Angers
CHU de Strasbourg
CHU de Grenoble
CHU de Rouen
CHU de Lille
CHU de Caen
CHU de Reims
CHRU de Tours
CHU de Poitiers
IPC Marseille
APHM Marseille
CHU de Nice
CHU de Clermont-Ferrand
Médipôle Cabestany
CH de Mont-de-Marsan
Clinique La Croix du Sud
Hôpital Privé Francheville
CH de Libourne
Pôle Santé Sud Atlantique
Clinique Pasteur
Clinique Santé Atlantique
CHU de Nîmes
Hôpital Privé des Côtes d’Armor
CHU de Nantes
Hôpital Privé La Louvière Lille
Clinique Nantes Atlantis
Clinique du Pont de Chaume
CHU de Dijon
ICANS
CH de Kourou – French Guiana
AP-HP Kremlin-Bicêtre
Hôpital Euro. Georges Pompidou
Hôpital Privé St-Joseph
AP-HP Henri-Mondor
AP-HP Pitié Salpêtrière
AP-HP Claude Bichat
Hôpital Tenon
AP-HP Claude Galien
Hôpital Foch
AP-HP Cochin Port-Royal
Prediction of pT3a upstaging in localized renal cell carcinoma
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UroCCR-15
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Study type: Observational
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Intervention: Retrospective analysis
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Clinical Trials Identifier: NCT03293563
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Publication: Boulenger de Hauteclocque et al. BJU Int. 2023
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Cohort: 4395 patients treated surgically for renal cell carcinoma between 2000-2019, either by laparoscopic (pure or robot-assisted) partial nephrectomy (PN), open PN, or laparoscopic radical nephrectomy (RN)
Prediction of kidney cancer recurrence after surgery
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UroCCR-120
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Study type: Observational
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Intervention: Retrospective analysis
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Clinical Trials Identifier: NCT03293563
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Publication: Margue et al. NPJ Precis Oncol. 2024
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Cohort: 3372 patients treated surgically for localized or locally advanced renal cell carcinoma between 2000-2020
Try UroPredict
Why?
Knowing individuals’ risk of tumor progression/upstaging can guide treatment decisions, enabling selection of the strategy with the best chance of treating the kidney cancer while minimizing side effects and maximizing quality of life.
Research is needed to develop and test such a model.
How?
Machine learning algorithms were designed, trained, and tested to predict kidney cancer upstaging and recurrence after surgery. As some data points might be missing in clinical routine, the algorithms were developed to handle incomplete data.
Model for pT3a upstaging (before surgery)
The participants were separated into two cohorts, training (n = 2636) and test (n = 1759). Seven pre-operative features were independently associated with pT3a upstaging and thus included in the predictive model:
- Tumor size
- Age
- Hilar location
- RENAL score
- Sex
- American Society of Anesthesiologists (ASA) score
- Symptoms at diagnosis
Seven machine learning algorithms were tested and logistic regression was found to be the most effective, with a prAUC of 0.41 on the test dataset and an area under the ROC curve of 0.77.
Model for disease recurrence (after surgery)
The dataset was split into two cohorts, training (n = 2241) and test (n = 1131). 24 clinical, pathological, and biological features were included in the predictive model, including:
- Tumor size
- Histological subtype
- Age
- Fuhrman grade
- Sex
- Pathological primary tumor staging
- Pathological regional lymph nodes staging
The final machine learning model surpassed the predictive performance of the most commonly used risk scores, with an integrated AUC of 0.79 (95% CI, 0.74–0.83) on the test dataset. The ML model had the further advantage of being able to handle incomplete data.
Applying the pT3a upstaging model
The model predicts individuals’ risk of renal cell carcinoma upstaging before surgery. As an example, the model predicted that one patient had a 32% probability of upstaging, higher than the average of tumor upstaging observed in this study (15%). Using SHAP values, we can interpret this elevated probability with risk factors (ASA score of 2, male sex, tumor size of 8 cm) counterbalanced by protective factors (young age, non-hilar location of tumor).
Applying the disease recurrence model
The model predicts the disease-free survival curve of each patient in the years following surgery, even in presence of incomplete values in predictors. It also assigns each patient to a risk group of recurrence or death within five years after surgery. Finally, using SHAP values, we are able to explain how each multimodal feature contributes to the patient-specific prediction.
What is the impact for patients?
The prediction model has the potential to support decision making around the treatment of clinically localized kidney tumors. Predicting risk of upstaging to pT3a could identify high-risk patients most likely to benefit from preoperative systemic therapy, or even low-risk patients for whom active surveillance could be sufficient.
What is the impact for patients?
The prediction model has the potential to support decision making around the management of patients undergoing localized or locally advanced kidney cancer. Predicting disease-free survival over the years following surgery could enhance the identification of patients candidate for adjuvant therapy or the identification of patients who could benefit from a less intensive post-operative follow-up.
Read our recently published research to discover more:
UroCCR-120: Margue G, et al. UroPredict: Machine learning model on real-world data for prediction of kidney cancer recurrence. NPJ Precis Oncol. 2024 Feb 23;8(1):45
UroCCR-120: Margue G, et al. Development of an individual postoperative prediction model for kidney cancer recurrence using machine learning. J Urol 2023 Vol. 209, No. 4S
UroCCR-15: Boulenger de Hauteclocque A, et al. Machine-learning approach for prediction of pT3a upstaging and outcomes of localized renal cell carcinoma. BJU Int. 2023 Aug;132(2):160-169
UroCCR-15: Boulenger de Hautecloque A, et al. Individualized prediction of post-surgical pathologic T3a (pT3a) upstaging risk in localized renal tumors undergoing nephrectomy. JCO 2022 40:16_suppl, 4547-4547
Margue G, et al. Disease-free survival in operated patients with nmRCC using machine learning. JCO 2023 41:16_suppl, 4539-4539