Search Results (40)

Background/Objectives: Adaptive radiotherapy (ART) is essential yet expensive in prostate cancer treatment. This study aims to preliminarily validate the feasibility of an integrated AI workflow for CBCT-guided adaptive radiotherapy in prostate cancer, and to provide a technical foundation for subsequent clinical translation. Methods: Planning CT and CBCT images from 120 patients were used for training and validation, while 21 patients were reserved for testing. A CycleGAN-ResNet generated synthetic CT (sCT) from CBCT, and an nnU-Net model performed auto-segmentation on the sCT. Image quality and segmentation accuracy were quantitatively assessed. The original plan was recalculated on sCT to evaluate actual dose delivery; if clinical constraints were unmet, adaptive re-optimization was performed, and plans were compared. Results: The total time per fraction in this study was approximately 19 ± 6 min, falling within the reported feasibility range for online ART. sCT image quality was significantly improved, making them suitable for subsequent auto-segmentation and treatment planning. The auto-segmentation technique substantially enhanced contouring efficiency, with the automatically generated contours requiring only minor modifications to meet clinical standards. In dosimetric analysis, the adaptive plans provided superior target coverage, CI, and HI. Compared with the actual dose delivered by the original plan, the adaptive plans yielded lower bladder V40 and lower rectal mean dose/V30/V40/V50. Conclusions: This study preliminarily validated the feasibility of an integrated AI workflow that concatenates CycleGAN-based sCT generation, nnU-Net-based auto-segmentation, and sCT-based adaptive plan re-optimization. Compared with conventional segmented studies, this integrated exploration facilitates a more comprehensive assessment of the potential value of AI technologies in CBCT-guided prostate cancer ART, offering a preliminary solution for promoting a cost-effective adaptive radiotherapy approach. Full article

Background/Objectives: This study evaluated the daily delivered dose in prostate cancer patients using the automated artificial intelligence (AI)-based software Adaptbox (v2.3.2, Therapanacea). The aim was to assess target coverage and organ-at-risk (OAR) exposure. Methods: Twenty patients were included. All received 80 Gy in 40 fractions to the prostate and 56 Gy simultaneously to the seminal vesicles using two-arc VMAT on a TrueBeam STx, with daily CBCT for setup. For each fraction, CBCT images were imported into Adaptbox. A synthetic CT (sCT) was generated using a deep learning algorithm. OARs were automatically segmented, while targets were propagated from the planning CT (pCT) using rigid registration. Dose calculation was performed using Adaptbox’s collapse-cone algorithm. Dose parameters were extracted for each session and compared with planned values. Results: All 800 fractions were analyzed. The planning target volume (PTV) remained consistent with planning, with a maximum deviation of 0.1% for both PTVs. For the rectum, 78.38%, 77.75%, and 78.13% of fractions exceeded planned doses for V_70Gy, V_76Gy and V_80Gy, respectively. One patient had five consecutive fractions with >5% deviation across all rectal metrics. For the bladder, 52.34% of fractions exceeded the planned V_80Gy, and two patients had ≥5 consecutive fractions with >5% deviation; however, this was attributed to contouring inaccuracies. Conclusions: This AI-based workflow enables reliable daily dose reconstruction and can identify clinically relevant OAR dose deviations that may support adaptive interventions, although accurate contouring remains essential. Full article

Purpose: The aim is to evaluate the effectiveness of artificial intelligence (AI)-based automatic segmentation as a predictive tool for clinical peer review in prostate cancer patients treated with hypofractionated radiotherapy. Methodology: A retrospective analysis was conducted on 62 patients treated across three Italian centers between 2020 and 2025. CT images were segmented using software based on 3D U-net models. Three workflows were compared: manual segmentation (C man), automatic segmentation (C AI), and AI-based segmentation adjusted by clinicians (C adj). Quantitative metrics used for comparison included the Dice Similarity Coefficient (DSC) and Hausdorff Distance (HDmax). Statistical analysis involved Welch’s t-test and Cohen’s d for effect size. Results: The results showed a significant improvement in agreement between C AI and C adj compared to C man. Median DSC for CTV increased from 0.80 (C man) to 0.92 (C adj), while HDmax decreased from 12.33 mm to 9.22 mm. Similar improvements were observed for the bladder and anorectum. All differences were statistically significant (p < 0.0001), with large effect sizes (Cohen’s d > 0.8). Discussion: AI use demonstrated a reduction in interobserver variability and segmentation time, enhancing workflow standardization. The C adj workflow, where the physician acts as a reviewer of AI-generated contours, proved effective and potentially integrable into clinical peer review. The predictive peer review refers to a preliminary support step in the clinical review process rather than a substitute for medical decision-making. Full article

Background/Objectives: Active surveillance (AS) has emerged as a safe alternative to primary therapy for low- and select intermediate-risk prostate cancer (PCa), but optimal patient selection and surveillance strategies remain challenging due to limited risk stratification tools enabling early detection of lesions with high potential for histopathological progression. This study presents an integrated method for predicting prostate cancer progression within 12 months, aiming to improve AS patient selection by categorizing patients into two risk groups: rapid progressors who would benefit from immediate treatment and slow progressors suitable for AS. Methods: The risk assessment platform combines convolutional neural networks for automatic segmentation of prostate and suspicious-for-cancer lesions on multiparametric MRI (mpMRI) with logistic regression to estimate progression risk. The networks were trained on annotated lesions from radical prostatectomy specimen mapped to mpMRI. The prediction model incorporated pre-biopsy clinical variables (age, PSA, PI-RADS) and MRI-derived intratumoral radiomic features from 163 participants of a prospective clinical trial, using histopathological progression within 12 months as endpoint. Results: The clinical-radiomics model achieved an AUC of 0.84 in distinguishing rapid from slow progressors, using non-invasive monitoring techniques. In an independent test set, the model significantly improved AS patient selection, increasing negative predictive value by 18.5% compared to current standard-of-care (p < 0.001). Conclusions: The risk assessment platform shows promise for use during annual follow-up visits to reliably differentiate suitable AS candidates with stable disease from PCa patients who are likely to experience early progression. Full article

Prostate cancer is one of the most lethal cancers in the male population, and accurate localization of intraprostatic lesions on MRI remains challenging. In this study, we investigated methods for improving prostate cancer segmentation on T2-weighted pelvic MRI using cascaded neural networks. We used an anonymized dataset of 400 multiparametric MRI scans from two centers, in which experienced radiologists had delineated the prostate and clinically significant cancer on the T2 series. Our baseline approach applies 2D and 3D segmentation networks (UNETR, UNET++, Swin-UNETR, SegResNetDS, and SegResNetVAE) directly to full MRI volumes. We then introduce additional stages that filter slices using DenseNet-201 classifiers (cancer/no-cancer and prostate/no-prostate) and localize the prostate via a YOLO-based detector to crop the 3D region of interest before segmentation. Using Swin-UNETR as the backbone, the prostate segmentation Dice score increased from 71.37% for direct 3D segmentation to 76.09% when using prostate detection and cropped 3D inputs. For cancer segmentation, the final cascaded pipeline—prostate detection, 3D prostate segmentation, and 3D cancer segmentation within the prostate—improved the Dice score from 55.03% for direct 3D segmentation to 67.11%, with an ROC AUC of 0.89 on the test set. These results suggest that cascaded detection- and segmentation-based preprocessing of the prostate region can substantially improve automatic prostate cancer segmentation on MRI while remaining compatible with standard segmentation architectures. Full article

Background: Precise delineation of the rectum is crucial in treatment planning for cancers in the pelvic region, such as prostate and cervical cancers. Manual segmentation is also still time-consuming and suffers from inter-observer variability. Since there are meaningful differences in rectal anatomy between males and females, incorporating sex-specific anatomical patterns can be used to enhance the performance of segmentations. Furthermore, recent deep learning advancements have provided promising solutions for automatically classifying patient sex from CT scans and leveraging this information for enhancing the accuracy of rectal segmentation. However, their clinical utility requires comprehensive validation against real-world standards. Methods: In this study, a two-stage deep learning pipeline was developed using CT scans from 186 patients with either prostate or cervical cancer. First, a CNN model automatically classified the patient’s biological sex from CT images in order to capture anatomical variations dependent on sex. Second, a sex-aware U-Net model performed automated rectal segmentation, allowing the network to adjust its feature representation based on the anatomical differences identified in stage one. The internal validation had an 80/20 train–test split, and 15% of the training portion was held out for validation to ensure balanced distribution regarding sex and diagnosis. Model performance was evaluated using spatial similarity metrics, including the Dice Similarity Coefficient (DSC), Hausdorff Distance, and Average Surface Distance. Additionally, a radiation oncologist conducted a retrospective clinical evaluation using a 3-point Likert scale. Statistical significance was examined using Wilcoxon signed-rank tests, Welch’s t-tests, and Mann–Whitney U test. Results: The sex-classification model attained an accuracy of 94.6% (AUC = 0.98, 95% CI: 0.96–0.99). Incorporation of predicted sex into the segmentation pipeline improved anatomical consistency of U-Net outputs. Mean DSC values were 0.91 (95% CI: 0.89–0.92) for prostate cases and 0.89 (95% CI: 0.87–0.91) for cervical cases, with no significant difference between groups (p = 0.12). Surface distance metrics calculated on resampled isotropic voxels showed mean HD values of 3.4 ± 0.8 mm and ASD of 1.2 ± 0.3 mm, consistent with clinically acceptable accuracy. On clinical evaluation, 89.2% of contours were rated as excellent, while 9.1% required only minor adjustments. Automated segmentation reduced the average contouring time from 12.7 ± 2.3 min manually to 4.3 ± 0.9 min. Conclusions: The proposed sex-aware deep learning framework offers accurate, robust segmentation of the rectum in pelvic CT imaging by explicitly modeling sex-specific differences in anatomical characteristics. This physiologically informed approach enhances segmentation performance and supports reliable integration of AI-based delineation into radiotherapy workflows to improve both contouring efficiency and clinical consistency. Full article

Robot-assisted radical prostatectomy (RARP) has become the most prevalent treatment for patients with organ-confined prostate cancer. Despite superior outcomes, suboptimal vesicourethral anastomosis (VUA) may lead to serious complications, including urinary leakage, prolonged catheterization, and extended hospitalization. A precise localization of both the surgical needle and the surrounding vesical and urethral tissues to coadapt is needed for fine-grained assessment of this task. Nonetheless, the identification of anatomical structures from endoscopic videos is difficult due to tissue distortions, changes in brightness, and instrument interferences. In this paper, we propose and compare two Deep Learning (DL) pipelines for the automatic segmentation of the mucosal layers and the suturing needle in real RARP videos by exploiting different architectures and training strategies. To train the models, we introduce a novel, annotated dataset collected from four VUA procedures. Experimental results show that the nnU-Net 2D model achieved the highest class-specific metrics, with a Dice Score of 0.663 for the mucosa class and 0.866 for the needle class, outperforming both transformer-based and baseline convolutional approaches on external validation video sequences. This work paves the way for computer-assisted tools that can objectively evaluate surgical performance during the critical phase of suturing tasks. Full article

Background: Most of the existing hyperpolarized (HP) ¹³C MRI analyses use univariate rate maps of pyruvate-to-lactate conversion (k_PL), and radiomic-style multiparametric models extracting complex, higher-order features remain unexplored. Purpose: To establish a multivariate framework based on whole abdomen/pelvis HP ¹³C-pyruvate MRI and evaluate the association between multiparametric features of metabolism (MFM) and clinical outcome measures in advanced and metastatic prostate cancer. Methods: Retrospective statistical analysis was performed on 16 participants with metastatic or local-regionally advanced prostate cancer prospectively enrolled in a tertiary center who underwent HP-pyruvate MRI of abdomen or pelvis between November 2020 and May 2023. Five patients were hormone-sensitive and eleven were castration-resistant. GMP-grade [1-¹³C]pyruvate was polarized using a 5T clinical-research DNP polarizer, and HP MRI used a set of flexible vest-transmit, array-receive coils, and echo-planar imaging sequences. Three basic metabolic maps (k_PL, pyruvate summed-over-time, and mean pyruvate time) were created by semi-automatic segmentation, from which 316 MFMs were extracted using an open-source, radiomic-compliant software package. Univariate risk classifier was constructed using a biologically meaningful feature (k_PL,median), and the multivariate classifier used a two-step feature selection process (ranking and clustering). Both were correlated with progression-free survival (PFS) and overall survival (OS) (median follow-up = 22.0 months) using Cox proportional hazards model. Results: In the univariate analysis, patients harboring tumors with lower-k_PL,median had longer PFS (11.2 vs. 0.5 months, p < 0.01) and OS (NR vs. 18.4 months, p < 0.05) than their higher-k_PL,median counterparts. Using a hypothesis-generating, age-adjusted multivariate risk classifier, the lower-risk subgroup also had longer PFS (NR vs. 2.4 months, p < 0.002) and OS (NR vs. 18.4 months, p < 0.05). By contrast, established laboratory markers, including PSA, lactate dehydrogenase, and alkaline phosphatase, were not significantly associated with PFS or OS (p > 0.05). Key limitations of this study include small sample size, retrospective study design, and referral bias. Conclusions: Risk classifiers derived from select multiparametric HP features were significantly associated with clinically meaningful outcome measures in this small, heterogeneous patient cohort, strongly supporting further investigation into their prognostic values. Full article

Background: Nuclei segmentation is the first stage of automated microscopic image analysis. The cell nucleus is a crucial aspect in segmenting to gain more insight into cell characteristics and functions that enable computer-aided pathology for early disease detection, such as prostate cancer, breast cancer, brain tumors, and other diagnoses. Nucleus segmentation remains a challenging task despite significant advancements in automated methods. Traditional techniques, such as Otsu thresholding and watershed approaches, are ineffective in challenging scenarios. However, deep learning-based methods exhibit remarkable results across various biological imaging modalities, including computational pathology. Methods: This work explores machine learning approaches for nuclei segmentation by evaluating the quality of nuclei image segmentation. We employed several methods, including K-means clustering, Random Forest (RF), Support Vector Machine (SVM) with handcrafted features, and Logistic Regression (LR) using features derived from Convolutional Neural Networks (CNNs). Handcrafted features extract attributes like the shape, texture, and intensity of nuclei and are meticulously developed based on specialized knowledge. Conversely, CNN-based features are automatically acquired representations that identify complex patterns in nuclei images. To assess how effectively these techniques segment cell nuclei, their performance is evaluated. Results: Experimental results show that Logistic Regression based on CNN-derived features outperforms the other techniques, achieving an accuracy of 96.90%, a Dice coefficient of 74.24, and a Jaccard coefficient of 55.61. In contrast, the Random Forest, Support Vector Machine, and K-means algorithms yielded lower segmentation performance metrics. Conclusions: The conclusions suggest that leveraging CNN-based features in conjunction with Logistic Regression significantly enhances the accuracy of cell nuclei segmentation in pathological images. This approach holds promise for refining computer-aided pathology workflows, potentially leading to more reliable and earlier disease diagnoses. Full article

This study introduces a deep learning-based strategy to automatically detect the L3 slice and segment abdominal tissues from computed tomography (CT) images. Accurate measurement of muscle and fat composition at the L3 level is critical as it can serve as a prognostic biomarker for cancer diagnosis and treatment. However, current manual approaches are time-consuming and prone to class imbalance, since L3 slices constitute only a small fraction of the entire CT dataset. In this study, we propose an optimization-incorporated strategy that integrates augmentation ratio and class weight adjustment as correction design variables within deep learning models. In this retrospective study, the CT dataset was privately collected from 150 prostate cancer and bladder cancer patients at the Department of Urology of Gangneung Asan Hospital. A ResNet50 classifier was used to detect the L3 slice, while standard Unet, Swin-Unet, and SegFormer models were employed to segment abdominal tissues. Bayesian optimization determines optimal augmentation ratios and class weights, mitigating the imbalanced distribution of L3 slices and abdominal tissues. Evaluation of CT data from 150 prostate and bladder cancer patients showed that the optimized models reduced the slice detection error to approximately 0.68 ± 1.26 slices and achieved a Dice coefficient of up to 0.987 ± 0.001 for abdominal tissue segmentation-improvements over the models that did not consider correction design variables. This study confirms that balancing class distribution and properly tuning model parameters enhances performance. The proposed approach may provide reliable and automated biomarkers for early cancer diagnosis and personalized treatment planning. Full article

Over the past decade, the development of computer-aided detection tools for medical image analysis has seen significant advancements. However, tasks such as the automatic differentiation of tissues or regions in medical images remain challenging. Magnetic resonance imaging (MRI) has proven valuable for early diagnosis, particularly in conditions like prostate cancer, yet it often struggles to produce high-resolution images with clearly defined boundaries. In this article, we propose a novel segmentation approach based on minimum cross-entropy thresholding using the equilibrium optimizer (MCE-EO) to enhance the visual differentiation of tissues in prostate MRI scans. To validate our method, we conducted two experiments. The first evaluated the overall performance of MCE-EO using standard grayscale benchmark images, while the second focused on a set of transaxial-cut prostate MRI scans. MCE-EO’s performance was compared against six stochastic optimization techniques. Statistical analysis of the results demonstrates that MCE-EO offers superior performance for prostate MRI segmentation, providing a more effective tool for distinguishing between various tissue types. Full article

The accurate segmentation of prostate cancer (PCa) from multiparametric MRI is crucial in clinical practice for guiding biopsy and treatment planning. Existing automated methods often lack the necessary accuracy and robustness in localizing PCa, whereas interactive segmentation methods, although more accurate, require user intervention on each input image, thereby limiting the cost-effectiveness of the segmentation workflow. Our innovative framework addresses the limitations of current methods by combining a coarse segmentation network, a rejection network, and an interactive deep network known as Segment Anything Model (SAM). The coarse segmentation network automatically generates initial segmentation results, which are evaluated by the rejection network to estimate their quality. Low-quality results are flagged for user interaction, with the user providing a region of interest (ROI) enclosing the lesions, whereas for high-quality results, ROIs were cropped from the automatic segmentation. Both manually and automatically defined ROIs are fed into SAM to produce the final fine segmentation. This approach significantly reduces the annotation burden and achieves substantial improvements by flagging approximately 20% of the images with the lowest quality scores for manual annotation. With only half of the images manually annotated, the final segmentation accuracy is statistically indistinguishable from that achieved using full manual annotation. Although this paper focuses on prostate lesion segmentation from multimodality MRI, the framework can be adapted to other medical image segmentation applications to improve segmentation efficiency while maintaining high accuracy standards. Full article

Accurate prostate volume estimation is crucial for effective prostate disease management. Ultrasound (US) imaging, particularly transrectal ultrasound, offers a cost-effective and rapid assessment. However, US images often suffer from artifacts and poor contrast, making prostate volume estimation challenging. This review explores recent advancements in deep learning (DL) techniques for automatic prostate segmentation in US images as a primary step toward prostate volume estimation. We examine various DL architectures, including traditional U-Net modifications and innovative designs incorporating residual connections, multi-directional image data, and attention mechanisms. Additionally, we discuss pre-processing methods to enhance image quality, the integration of shape information, and strategies to improve the consistency and robustness of DL models. The effectiveness of these techniques is evaluated through metrics such as the Dice Similarity Coefficient, Jaccard Index, and Hausdorff Distance. The review highlights the potential of DL in improving prostate volume estimation accuracy and reducing clinical workload while also identifying areas for future research to enhance model performance and generalizability. Full article

Background: Whole-Body Diffusion-Weighted Imaging (WBDWI) is an established technique for staging and evaluating treatment response in patients with multiple myeloma (MM) and advanced prostate cancer (APC). However, WBDWI scans show inter- and intra-patient intensity signal variability. This variability poses challenges in accurately quantifying bone disease, tracking changes over follow-up scans, and developing automated tools for bone lesion delineation. Here, we propose a novel automated pipeline for inter-station, inter-scan image signal standardisation on WBDWI that utilizes robust segmentation of the spinal canal through deep learning. Methods: We trained and validated a supervised 2D U-Net model to automatically delineate the spinal canal (both the spinal cord and surrounding cerebrospinal fluid, CSF) in an initial cohort of 40 patients who underwent WBDWI for treatment response evaluation (80 scans in total). Expert-validated contours were used as the target standard. The algorithm was further semi-quantitatively validated on four additional datasets (three internal, one external, 207 scans total) by comparing the distributions of average apparent diffusion coefficient (ADC) and volume of the spinal cord derived from a two-component Gaussian mixture model of segmented regions. Our pipeline subsequently standardises WBDWI signal intensity through two stages: (i) normalisation of signal between imaging stations within each patient through histogram equalisation of slices acquired on either side of the station gap, and (ii) inter-scan normalisation through histogram equalisation of the signal derived within segmented spinal canal regions. This approach was semi-quantitatively validated in all scans available to the study (N = 287). Results: The test dice score, precision, and recall of the spinal canal segmentation model were all above 0.87 when compared to manual delineation. The average ADC for the spinal cord (1.7 × 10⁻³ mm²/s) showed no significant difference from the manual contours. Furthermore, no significant differences were found between the average ADC values of the spinal cord across the additional four datasets. The signal-normalised, high-b-value images were visualised using a fixed contrast window level and demonstrated qualitatively better signal homogeneity across scans than scans that were not signal-normalised. Conclusion: Our proposed intensity signal WBDWI normalisation pipeline successfully harmonises intensity values across multi-centre cohorts. The computational time required is less than 10 s, preserving contrast-to-noise and signal-to-noise ratios in axial diffusion-weighted images. Importantly, no changes to the clinical MRI protocol are expected, and there is no need for additional reference MRI data or follow-up scans. Full article