Publications by authors named "Jan Peeken"
The Co-IMPACT consortium addresses knowledge gaps in prostate-specific membrane antigen positron emission tomography-guided radiotherapy for prostate cancer by establishing a global database (46 centres from 16 countries) to standardise and analyse data across four distinguished clinical scenarios. A collaborative model with the Advanced Prostate Cancer Consensus Conference aligns urgent clinical needs with actionable research insights.
View Article and Find Full Text PDFProstate-specific membrane antigen (PSMA)-targeted positron emission tomography (PET) has improved localization of prostate cancer (PC) lesions in biochemical recurrence (BCR) for salvage radiotherapy (SRT). We conducted a retrospective review of patients undergoing F-rhPSMA-7 or F-flotufolastat (F-rhPSMA-7.3)-PET-guided SRT compared with conventional-SRT (C-SRT) without PET.
View Article and Find Full Text PDFPurpose: General practitioners (GPs) play a crucial role in providing interdisciplinary care for radiation oncology patients. This study aims to understand the specific needs and challenges faced by general practitioners in Germany when treating oncology patients.
Methods: A comprehensive web-based questionnaire with 24 items was disseminated to GPs in Germany via email using survio.
Article Synopsis
- Despite improvements in targeted therapy for Acute Myeloid Leukemia (AML), the prognosis remains poor, particularly for patients with relapsed or refractory disease.
- Allogeneic hematopoietic stem cell transplantation (alloSCT) is the main curative option for high-risk patients, but the best conditioning approach is still uncertain for those who are chemotherapy-refractory.
- A study on seven AML patients who received CXCR4-directed endoradiotherapy (ERT) combined with total body irradiation and chemotherapy prior to alloSCT showed promising outcomes, with 6 out of 7 patients achieving response and successful engraftment, offering insights into a potentially effective treatment strategy for advanced cases.
Background And Objective: Up to 50% of patients with prostate cancer experience prostate-specific antigen (PSA) relapse following primary radical prostatectomy (RP). Prostate-specific membrane antigen (PSMA) positron emission tomography (PET) is increasingly being used for staging after RP owing to its high detection rate. Our aim was to compare outcomes for patients who received salvage radiotherapy (sRT) with versus without PSMA PET guidance.
View Article and Find Full Text PDFBackground: Post-Therapy-Pneumonitis (PTP) is a critical side effect of both, thoracic radio(chemo)therapy (R(C)T) and immune checkpoint inhibition (ICI). However, disease characteristics and patient-specific risk factors of PTP after combined R(C)T + ICI are less understood. Given that RT-triggered PTP is strongly dependent on the volume and dose of RT [1], driven by inflammatory mechanisms, we hypothesize that combination therapy of R(C)T with ICI influences the dose-volume-effect correlation for PTP.
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- A subgroup of patients with oligometastatic cancer may benefit from local treatment of all cancer lesions to achieve longer disease-free survival, especially when brain metastases are involved.
- An analysis of 7,000 PET scans identified 106 patients with both extracranial oligometastases and brain metastases, finding that brain involvement significantly impacted disease classification and treatment outcomes.
- Patients with oligometastasic disease had a median survival of 28 months compared to 10 months for polymetastatic patients, suggesting that brain metastases should not automatically exclude individuals from clinical trials.
Article Synopsis
- Salvage radiation therapy (sRT) is crucial for patients who experience biochemical recurrence after prostate surgery, and a new nomogram has been developed to predict their chances of remaining free from this recurrence.
- * This study aims to evaluate the effectiveness of PSMA-PET-based assessments in guiding sRT for cases of prostate-specific antigen (PSA) persistence or recurrence, and it seeks to improve predictive models using random survival forests compared to traditional Cox models.
- * Data from 1029 patients across five countries were analyzed to validate these predictive models, utilizing machine learning techniques to better understand outcomes related to biochemical failure after treatment.
Article Synopsis
- Skin inflammation and conditions like moist epitheliolysis and edema are common acute side effects of breast radiotherapy (RT).
- The study aimed to evaluate the effectiveness of tissue-derived radiomics features compared to total breast volume (TBV) in predicting these side effects.
- The best predictive model used a LASSO classifier based on TBV, achieving an AUROC of 0.74, similar to the AUROC of 0.75 for TBV alone, with mammary tissue showing greater predictive power than glandular tissue.
Article Synopsis
- - The article discusses the increasing role of artificial intelligence (AI) in radiation oncology, specifically its application in patient care and radiotherapy planning.
- - It reviews AI techniques for automating the segmentation of important areas like organs at risk (OARs) and tumor volumes, highlighting improved efficiency and consistency in treatment planning.
- - Despite challenges in applying these tools clinically, the potential for personalized treatment plans and advancements in tumor detection presents a promising future for faster and more precise radiotherapy.
Background: An aberrant cellular microenvironment characterized by pathological cells or inflammation represents an added risk factor across various cancer types. While the significance of chronic inflammation in the development of most diffuse tumors has been extensively studied, an exception to this analysis exists in the context of chondrosarcomas. Chondrosarcomas account for 20-30% of all bone sarcomas, with an estimated global incidence of 1 in 100,000.
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- - Meningiomas are the most common primary brain tumors, with most being benign, but around 25% are higher-grade and require better risk assessment through an integrated risk score (IRS) based on tumor biology.
- - The study involved 160 patients and utilized machine learning with preoperative MRI scans to develop classifiers that predict the IRS, achieving a high accuracy of 90% when distinguishing low-risk from medium/high-risk patients.
- - The results highlight that specific imaging characteristics, like "sphericity," can effectively predict the molecular low-risk classification of meningiomas, making critical prognostic information more accessible through imaging techniques.
Purpose: Despite growing evidence for bilateral pelvic radiotherapy (whole pelvis RT, WPRT) there is almost no data on unilateral RT (hemi pelvis RT, HPRT) in patients with nodal recurrent prostate cancer after prostatectomy. Nevertheless, in clinical practice HPRT is sometimes used with the intention to reduce side effects compared to WPRT. Prostate-specific membrane antigen positron emission tomography / computed tomography (PSMA-PET/CT) is currently the best imaging modality in this clinical situation.
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- Surgical resection is the primary treatment for patients with large or symptomatic brain metastases, but there's still a risk of local failure, prompting the development of a prediction tool to identify those at high risk.
- Data from the AURORA study included 253 patients for training and 99 for external testing, utilizing radiomic features from MRI scans to enhance prediction accuracy.
- The elastic net regression model combining radiomic and clinical features showed a significant improvement in predicting local failure, with lower risk groups experiencing only 9% failure at 24 months compared to 74% in high-risk groups, suggesting potential for improved patient follow-up and treatment.
Article Synopsis
- The study focuses on developing a Deep Learning-based automatic segmentation (DLBAS) algorithm to improve the process of defining volume of interest (VOI) for radiomic analyses in extremity soft tissue sarcomas, addressing issues like time consumption and variability among observers.* -
- The DLBAS was trained on 157 patients and tested on 87, comparing its automatic segmentations to manual delineations by radiation oncologists and residents, showing promising median dice similarity coefficients that indicate high accuracy in VOI predictions.* -
- Despite achieving high reproducibility for radiomics feature extraction, the clinical applicability of the DLBAS predictions for radiotherapy planning was found to be limited, with radiation oncologists deeming them suitable in only
Article Synopsis
- The study focuses on the importance of identifying internal mammary lymph node (IMN) metastases in breast cancer patients to optimize treatment strategies like regional lymph node irradiation.
- Researchers analyzed a total of 297 patients with breast cancer who showed evidence of IMN and/or axillary metastases, assessing tumor contact with internal mammary perforating vessels (IMPV) using advanced MRI techniques.
- Results indicated a strong correlation between major IMPV contact and the presence of gross IMN involvement, with the predictive accuracy of major IMPV contact surpassing traditional clinical models, suggesting it could be a key risk factor in determining treatment plans.
Purpose: The European Association of Urology (EAU) proposed a risk stratification (high vs. low risk) for patients with biochemical recurrence (BR) following radical prostatectomy (RP). Here we investigated whether this stratification accurately predicts outcome, particularly in patients staged with PSMA-PET.
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- The study focuses on improving treatment for glioblastoma, a challenging brain cancer, by validating a new computational tumor growth model for personalized therapy.
- Researchers analyzed data from 124 TCGA patients and 397 UCSF patients to find links between clinical outcomes and factors related to tumor growth and genetics.
- Results indicate that certain growth parameters are significantly linked to patient survival and that the model may enhance radiation treatment planning without increasing radiation exposure.
Objectives: Post-therapy pneumonitis (PTP) is a relevant side effect of thoracic radiotherapy and immunotherapy with checkpoint inhibitors (ICI). The influence of the combination of both, including dose fractionation schemes on PTP development is still unclear. This study aims to improve the PTP risk estimation after radio(chemo)therapy (R(C)T) for lung cancer with and without ICI by investigation of the impact of dose fractionation on machine learning (ML)-based prediction.
View Article and Find Full Text PDFNeural-network-based outcome predictions may enable further treatment personalization of patients with head and neck cancer. The development of neural networks can prove challenging when a limited number of cases is available. Therefore, we investigated whether multitask learning strategies, implemented through the simultaneous optimization of two distinct outcome objectives (multi-outcome) and combined with a tumor segmentation task, can lead to improved performance of convolutional neural networks (CNNs) and vision transformers (ViTs).
View Article and Find Full Text PDFPatients suffering from painful spinal bone metastases (PSBMs) often undergo palliative radiation therapy (RT), with an efficacy of approximately two thirds of patients. In this exploratory investigation, we assessed the effectiveness of machine learning (ML) models trained on radiomics, semantic and clinical features to estimate complete pain response. Gross tumour volumes (GTV) and clinical target volumes (CTV) of 261 PSBMs were segmented on planning computed tomography (CT) scans.
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- This study looked at how well salvage radiotherapy (SRT) works for men with early recurring prostate cancer after surgery, even when their scans show no signs of the disease.
- It involved 300 patients from 11 different centers in 5 countries and checked their survival rates after SRT treatment.
- The results showed that most patients had good outcomes, with high chances of staying cancer-free for years, especially when they had lower PSA levels before treatment.
Background: Many automatic approaches to brain tumor segmentation employ multiple magnetic resonance imaging (MRI) sequences. The goal of this project was to compare different combinations of input sequences to determine which MRI sequences are needed for effective automated brain metastasis (BM) segmentation.
Methods: We analyzed preoperative imaging (T1-weighted sequence ± contrast-enhancement (T1/T1-CE), T2-weighted sequence (T2), and T2 fluid-attenuated inversion recovery (T2-FLAIR) sequence) from 339 patients with BMs from seven centers.
Importance: Prostate-specific antigen membrane positron-emission tomography (PSMA-PET) is increasingly used to guide salvage radiotherapy (sRT) after radical prostatectomy for patients with recurrent or persistent prostate cancer.
Objective: To develop and validate a nomogram for prediction of freedom from biochemical failure (FFBF) after PSMA-PET-based sRT.
Design, Setting, And Participants: This retrospective cohort study included 1029 patients with prostate cancer treated between July 1, 2013, and June 30, 2020, at 11 centers from 5 countries.
Background/purpose: The present study aimed to assess whether SRT to the prostatic fossa should be initiated in a timely manner after detecting biochemical recurrence (BR) in patients with prostate cancer, when no correlate was identified with prostate-specific membrane antigen positron emission tomography (PSMA-PET).
Materials And Methods: This retrospective, multicenter analysis included 1222 patients referred for PSMA-PET after a radical prostatectomy due to BR. Exclusion criteria were: pathological lymph node metastases, prostate-specific antigen (PSA) persistence, distant or lymph node metastases, nodal irradiation, and androgen deprivation therapy (ADT).