# ProstNFound+: A Prospective Study using Medical Foundation Models for Prostate Cancer Detection

arXiv: https://arxiv.org/abs/2510.26703 · HTML: https://mlsanity.com/paper/2510.26703/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 30 Oct 2025.

## Builds on (most cited first, 8 of 8)

- [Conditional Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2203.05557/) (2022)
- [Segment Anything in Medical Images](https://mlsanity.com/paper/2304.12306/) (2023)
- [USFM: A Universal Ultrasound Foundation Model Generalized to Tasks and Organs towards Label Efficient Image Analysis](https://mlsanity.com/paper/2401.00153/) (2023)
- [MicroSegNet: A Deep Learning Approach for Prostate Segmentation on Micro-Ultrasound Images](https://mlsanity.com/paper/2305.19956/) (2023)
- [Prompt Tuning for Parameter-efficient Medical Image Segmentation](https://mlsanity.com/paper/2211.09233/) (2022)
- [Self-Supervised Learning with Limited Labeled Data for Prostate Cancer Detection in High Frequency Ultrasound](https://mlsanity.com/paper/2211.00527/) (2022)
- [Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision](https://mlsanity.com/paper/2409.20293/) (2024)
- [Cinepro: Robust Training of Foundation Models for Cancer Detection in Prostate Ultrasound Cineloops](https://mlsanity.com/paper/2501.12331/) (2025)

## Built on by (most cited first, 5 of 5)

- [GUIDE-US: Grade-Informed Unpaired Distillation of Encoder Knowledge from Histopathology to Micro-UltraSound](https://mlsanity.com/paper/2602.19005/) (2026)
- [Learning Where to Look: A Reinforcement Learning Framework for Robust Micro-Ultrasound Prostate Cancer Detection](https://mlsanity.com/paper/2606.30951/) (2026)
- [Compass: Prostate Cancer Detection Needs Multi-View Context](https://mlsanity.com/paper/2607.06919/) (2026)
- [Learning Prostate Anatomy at Test Time for Cancer Detection in Micro-Ultrasound](https://mlsanity.com/paper/2608.20557/) (2026)
- [Weakly Supervised Spatial Grounding for Discriminative Attention-Based Ultrasound-Histopathology Alignment in Prostate Cancer Grading](https://mlsanity.com/paper/2609.15150/) (2026)

## Abstract (arXiv)

Purpose: Medical foundation models (FMs) offer a path to build high-performance diagnostic systems. However, their application to prostate cancer (PCa) detection from micro-ultrasound (μUS) remains untested in clinical settings. We present ProstNFound+, an adaptation of FMs for PCa detection from μUS, along with its first prospective validation. Methods: ProstNFound+ incorporates a medical FM, adapter tuning, and a custom prompt encoder that embeds PCa-specific clinical biomarkers. The model generates a cancer heatmap and a risk score for clinically significant PCa. Following training on multi-center retrospective data, the model is prospectively evaluated on data acquired five years later from a new clinical site. Model predictions are benchmarked against standard clinical scoring protocols (PRI-MUS and PI-RADS). Results: ProstNFound+ shows strong generalization to the prospective data, with no performance degradation compared to retrospective evaluation. It aligns closely with clinical scores and produces interpretable heatmaps consistent with biopsy-confirmed lesions. Conclusion: The results highlight its potential for clinical deployment, offering a scalable and interpretable alternative to expert-driven protocols.