Maximizing T2-Only Prostate Cancer Localization from Expected Diffusion Weighted Imaging

• URL: http://arxiv.org/abs/2604.00985v1
• Date: Wed, 01 Apr 2026 14:50:18 GMT
• Title: Maximizing T2-Only Prostate Cancer Localization from Expected Diffusion Weighted Imaging
• Abstract summary: Multiparametric MRI is increasingly recommended as a first-line noninvasive approach to detect and localize prostate cancer.<n>Early machine learning attempts using only T2w images have shown promising diagnostic performance in segmenting radiologist-annotated lesions.<n>This work investigates an arguably more challenging application using only T2w at inference, but to localize individual cancers based on independent histopathology labels.
• Score: 68.57480103120561
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multiparametric MRI is increasingly recommended as a first-line noninvasive approach to detect and localize prostate cancer, requiring at minimum diffusion-weighted (DWI) and T2-weighted (T2w) MR sequences. Early machine learning attempts using only T2w images have shown promising diagnostic performance in segmenting radiologist-annotated lesions. Such uni-modal T2-only approaches deliver substantial clinical benefits by reducing costs and expertise required to acquire other sequences. This work investigates an arguably more challenging application using only T2w at inference, but to localize individual cancers based on independent histopathology labels. We formulate DWI images as a latent modality (readily available during training) to classify cancer presence at local Barzell zones, given only T2w images as input. In the resulting expectation-maximization algorithm, a latent modality generator (implemented using a flow matching-based generative model) approximates the latent DWI image posterior distribution in the E-steps, while in M-steps a cancer localizer is simultaneously optimized with the generative model to maximize the expected likelihood of cancer presence. The proposed approach provides a novel theoretical framework for learning from a privileged DWI modality, yielding superior cancer localization performance compared to approaches that lack training DWI images or existing frameworks for privileged learning and incomplete modalities. The proposed T2-only methods perform competitively or better than baseline methods using multiple input sequences (e.g., improving the patient-level F1 score by 14.4\% and zone-level QWK by 5.3\% over the T2w+DWI baseline). We present quantitative evaluations using internal and external datasets from 4,133 prostate cancer patients with histopathology-verified labels.

Related papers

• Gaussian Meta-Space Augmentation for Stacking Ensembles in Multimodal IPMN Risk Stratification [12.244907298771585]
  We present cUPMI, a class-conditional Gaussian augmentation of a combiner's log-probability meta-features.<n>We find cUPMI adds limited value to properly regularized L2-logistic binary classification stacks, but consistently regularizes higher-capacity tree combiners.<n>Its cleanest ordinal benefit appears for XGBoost on an 8-stream radiomics task.
  arXiv  Detail & Related papers  (2026-08-11T22:17:32Z)
• Bootstrapped Physically-Primed Neural Networks for Robust T2 Distribution Estimation in Low-SNR Pancreatic MRI [1.2899392854599374]
  We introduce a bootstrap-based inference framework for robust distributional T2 estimation.<n>Our method uses inference-time bootstrapping to smooth noise artifacts and enhance fidelity to the underlying relaxation distribution.<n>These results establish inference-time bootstrapping as an effective enhancement for quantitative T2 relaxometry in low-SNR abdominal imaging.
  arXiv  Detail & Related papers  (2026-03-14T19:10:46Z)
• R$^{2}$Seg: Training-Free OOD Medical Tumor Segmentation via Anatomical Reasoning and Statistical Rejection [45.33258156568741]
  Foundation models for medical image segmentation struggle under out-of-distribution shifts.<n>We introduce R$2$Seg, a training-free framework for robust OOD tumor segmentation.
  arXiv  Detail & Related papers  (2025-11-16T17:15:52Z)
• T2-Only Prostate Cancer Prediction by Meta-Learning from Bi-Parametric MR Imaging [39.64252838533947]
  Current imaging-based prostate cancer diagnosis requires both MR T2-weighted (T2w) and diffusion-weighted imaging (DWI) sequences. measuring diffusion patterns in DWI sequences can be time-consuming, prone to artifacts and sensitive to imaging parameters. This study investigates the potential of machine learning (ML) methods using only the T2w sequence as input during inference time.
  arXiv  Detail & Related papers  (2024-11-11T22:38:45Z)
• Domain Transfer Through Image-to-Image Translation for Uncertainty-Aware Prostate Cancer Classification [42.75911994044675]
  We present a novel approach for unpaired image-to-image translation of prostate MRIs and an uncertainty-aware training approach for classifying clinically significant PCa. Our approach involves a novel pipeline for translating unpaired 3.0T multi-parametric prostate MRIs to 1.5T, thereby augmenting the available training data. Our experiments demonstrate that the proposed method significantly improves the Area Under ROC Curve (AUC) by over 20% compared to the previous work.
  arXiv  Detail & Related papers  (2023-07-02T05:26:54Z)
• Moving from 2D to 3D: volumetric medical image classification for rectal cancer staging [62.346649719614]
  preoperative discrimination between T2 and T3 stages is arguably both the most challenging and clinically significant task for rectal cancer treatment. We present a volumetric convolutional neural network to accurately discriminate T2 from T3 stage rectal cancer with rectal MR volumes.
  arXiv  Detail & Related papers  (2022-09-13T07:10:14Z)
• Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis [68.5287824124996]
  We present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images. We apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem. Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T08:41:17Z)
• Self-transfer learning via patches: A prostate cancer triage approach based on bi-parametric MRI [1.3934382972253603]
  Prostate cancer (PCa) is the second most common cancer diagnosed among men worldwide. The current PCa diagnostic pathway comes at the cost of substantial overdiagnosis, leading to unnecessary treatment and further testing. We present a patch-based pre-training strategy to distinguish between clinically significant (cS) and non-clinically significant (ncS) lesions.
  arXiv  Detail & Related papers  (2021-07-22T17:02:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.