Predicting prostate cancer recurrence using an atlas-based tumor control probability model

Abstract

Background: Recurrence following prostate cancer (PCa) radiation therapy (RT) remains a persistent challenge. Although dose escalation can improve tumor control, it often results in increased toxicity. With an understanding of tumor heterogeneity, identification of radioresistant tumor subvolumes at risk of low tumor control probability (TCP) could provide an opportunity for personalized dose prescription to reduce risk of treatment failure without the increased risk of toxicity.

Purpose: The aim of this study was to evaluate the efficacy of an atlas-based tumor control probability model in predicting prostate cancer recurrence by retrospectively integrating patient-specific primary radiotherapy and histopathology-informed data. A segment-wise adjustment to TCP model parameters was investigated for enhancing recurrence prediction in a patient cohort with biopsy-confirmed local recurrence following definitive RT.

Methods: Nine patients with biopsy proven local recurrence were selected from an ethics-approved study (NCT03073278) based on the availability of histopathology reports, dose-fractionation schedules and treatment planning data from their primary RT. Two previously-reported population-based biological atlases, one comprising a cell density data (CD-atlas) and the other tumor probability data (TP-atlas), were deformably registered to the prostate contour of each patient. Histopathology reports were retrieved for each patient, and the registered prostate atlases were anatomically segmented based on individual histopathology findings. Radiosensitivity parameters were derived from a separate patient cohort's histology dataset using a numerical optimization method, generating a single PCa grade-independent α/β ratio, four Gleason Pattern (GP)-dependent α parameters, and nine Gleason Score (GS)-dependent α/β ratios. Three parameter adjustment approaches-cell density alone, cell density with GP-dependent α, and cell density with GS-dependent α/β, were evaluated and compared to a baseline model without adjustments. Changes in overall TCP values resulting from the adjustments were analyzed, and recurrent gross tumor volume (GTV) contours were overlaid on the TCP maps to evaluate their alignment with regions of lower TCP, assessing the model's ability to enhance recurrence prediction.

Results: The approach combining segment-wise cell density and GS-dependent α/β adjustments showed superior predictive capability, with all nine patients (100%) exhibiting a significant (p = 0.004) reduction in overall TCP values and seven patients (78%) showing alignment of lower TCP regions with relapsed tumor sites. This was further supported by voxel-level histogram analysis and statistically significant volume-weighted TCP differences between GTV and nonGTV regions (Wilcoxon signed-rank test, p = 0.003). In contrast, GP-dependent α adjustments alongside cell density failed to predict recurrence, while cell density adjustments alone yielded moderate performance. Additionally, the generated single α/β ratio and GS-dependent α/β ratios were consistent with the lower α/β ratios typically associated with PCa.

Conclusions: The atlas-based TCP model, enhanced with patient-specific histopathology report data, demonstrated promising capabilities in predicting PCa recurrence. This approach has the potential to support personalized treatment planning by enabling optimization of the distribution of a specific integral dose to minimize recurrence risk.

Keywords: prostate cancer recurrence; radiosensitivity parameters; tumor control probability.

FIGURE 1

A flowchart displaying various steps from the methodology to calculate overall TCP and generate TCP distribution maps for the analysis.

FIGURE 2

A diagram of prostate cell density atlas divided into six segments (right base, right mid, right apex, left base, left mid, and left apex) following the biopsy report of Patient‐1. Each segment is assigned with a Gleason Score and percentage of cancerous cells found in the core biopsy. The color scale represents cell density, measured as the number of cells per unit voxel volume.

FIGURE 3

Axial views of TCP maps generated for Patient‐1 using (a) Method 1: No parameter adjustment, (b) Method 2: Cell density adjustment only, (c) Method 3: Cell density + GP‐dependent α adjustment, and (d) Method 4: Cell density + GS‐dependent α/β ratio. The darker regions indicate lower TCP values and the red contour line delineates the relapsed Gross Tumor Volume (GTV). Each subfigure is accompanied by a color bar (right) indicating voxel‐level TCP values.

FIGURE 4

Sensitivity Analysis: TCP reduction corresponding to α _GP2, α _GP3, α _GP4, and α _GP5 values computed by Zhao et al. for α/β ratios: 1.8 Gy (Dearnaley et al. ²⁰ ), 3.1 Gy (Wang et al. ¹⁹ ) and 8.3 Gy (Valdagni et al. ²⁷ ).

FIGURE 5

Histograms of voxel‐level TCP values for Patient‐1 using (a) Method 1 and (b) Method 4, corresponding to the TCP maps in Figure 3a,d, respectively. Similarly, histograms for Patient‐5 are shown for (c) Method 1 and (d) Method 4, with the corresponding TCP maps displayed in (e) and (f), respectively.

FIGURE 6

Volume‐weighted TCP comparisons between the relapsed GTV and nonGTV regions for each of the nine patients for (a): Method 1 and (b): Method 4. The dotted circle in (a) highlights a relatively lower volume‐weighted TCP in the nonGTV region compared to the GTV for Patient‐5 and Patient‐6 in Method 1.