Abstract 2013P
Background
Decisions on the best available treatment in clinical oncology are based on expert opinions in multidisciplinary cancer conferences (MCC). For this purpose, artificial intelligence (AI) could increase evidence-based treatment in clinical oncology as assistance system to give an additional treatment recommendation in MCC. Here, we present the first preliminary data for urothelial carcinoma (UC) patients AI-generated treatment recommendations.
Methods
We have transformed comprehensive patient data (107 individual features) of 1029 MCCs for UC from the years 2015 - 2022 into representations that can be used in software development. Next, we developed a two-step process in order to train a classifier to mimic the MMC recommendations. In the first step, we identified superordinate categories of the recommendations. In the second step, we specified the detailed recommendation. For this purpose, we used different machine learning (CatBoost, XGBoost, Random Forest) and deep learning (TabPFN, TabNet) approaches. Accuracy weights are determined by F1-Score.
Results
We developed an AI system which is able to decide which kind of superordinate recommendation should be applied, e.g. surgery or anticancer-drugs (Table). Furthermore, our AI system is able to suggest the specific surgical treatment as well as the correct drugs. Finally, the results show that our selected deep neural network architectures are able to learn from the limited amount of data. Table: 2013P
Accuracy rates for AI-generated treatment recommendations of urothelial cancer based on F1-Scores
| Task | F1-Score ↑ | #Classes | Class | F1-Score |
| 1st Step | 0.7912 | 5 | Surgery Medication Aftercare Chemoradiotherapy Best supportive care | 0.8831 0.8842 0.5714 0.0 0.6667 |
| 2nd Step: Surgical Prediction | 0.6500 | 5 | Cystectomy Cystoprostatectomy TURBT Nephrectomy Nephroureterectomy | 0.7660 0.5 0.5714 0.0 0.6667 |
| 3rd Step: Drug Prediction | 0.6880 | 12 | Gemcitabine/Cisplatin Gemcitabine/Carboplatin Vinflunine Paclitaxel Avelumab Pembrolizumab BCG Mitomycin Carboplatin Nab-Paclitaxel Paclitaxel/Gemcitabine Enfortumab-Vedotin | 0.9804 0.5 0.0 0.0 0.0 0.6667 0.6667 0.0 0.0 1.0 0.0 0.6667 |
Conclusions
To our knowledge, we present the first time data for fully automated AI-based treatment recommendations for MMC in urothelial cancer with excellent accuracy rates. In future, we aim to implement clinical trial data to enable an explainable AI for the generated recommendations.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
German Federal Ministry of Education and Research (BMBF, Grant number: 16SV9053).
Disclosure
All authors have declared no conflicts of interest.
Resources from the same session
2019P - Disparities in urothelial carcinoma (UC) drug approval: Contrasting North America and Europe
Presenter: Jose Tapia
Session: Poster session 13
2020TiP - SOGUG-NEOWIN: A phase II, open-label, multi-centre trial evaluating the efficacy and safety of erdafitinib (ERDA) monotherapy and ERDA and cetrelimab (CET) as neoadjuvant treatment in cisplatin-ineligible patients with muscle-invasive bladder cancer (MIBC) and FGFR gene alterations
Presenter: Yohann Loriot
Session: Poster session 13
2022TiP - Stereotactic treatment with neoadjuvant radiotherapy and enfortumab vedotin: A phase I/II study for localized, cisplatin ineligible, muscle invasive bladder cancer (STAR-EV)
Presenter: Tian Zhang
Session: Poster session 13
2024TiP - NETOS: A personalized approach of neoadjuvant therapy, including INCB099280 monotherapy and bladder preservation, for muscle-invasive urothelial bladder carcinoma (MIBC) with ctDNA monitoring
Presenter: Valentina Tateo
Session: Poster session 13