Publications by authors named "Philippe Lambin"

461 Publications

Machine learning for grading and prognosis of esophageal dysplasia using mass spectrometry and histological imaging.

Comput Biol Med 2021 Oct 4;138:104918. Epub 2021 Oct 4.

Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands; Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6202 AZ, Maastricht, The Netherlands.

Background: Barrett's esophagus (BE) is a precursor lesion of esophageal adenocarcinoma and may progress from non-dysplastic through low-grade dysplasia (LGD) to high-grade dysplasia (HGD) and cancer. Grading BE is of crucial prognostic value and is currently based on the subjective evaluation of biopsies. This study aims to investigate the potential of machine learning (ML) using spatially resolved molecular data from mass spectrometry imaging (MSI) and histological data from microscopic hematoxylin and eosin (H&E)-stained imaging for computer-aided diagnosis and prognosis of BE.

Methods: Biopsies from 57 patients were considered, divided into non-dysplastic (n = 15), LGD non-progressive (n = 14), LGD progressive (n = 14), and HGD (n = 14). MSI experiments were conducted at 50 × 50 μm spatial resolution per pixel corresponding to a tile size of 96x96 pixels in the co-registered H&E images, making a total of 144,823 tiles for the whole dataset.

Results: ML models were trained to distinguish epithelial tissue from stroma with area-under-the-curve (AUC) values of 0.89 (MSI) and 0.95 (H&E)) and dysplastic grade (AUC of 0.97 (MSI) and 0.85 (H&E)) on a tile level, and low-grade progressors from non-progressors on a patient level (accuracies of 0.72 (MSI) and 0.48 (H&E)).

Conclusions: In summary, while the H&E-based classifier was best at distinguishing tissue types, the MSI-based model was more accurate at distinguishing dysplastic grades and patients at progression risk, which demonstrates the complementarity of both approaches. Data are available via ProteomeXchange with identifier PXD028949.
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http://dx.doi.org/10.1016/j.compbiomed.2021.104918DOI Listing
October 2021

Selectively Targeting Tumor Hypoxia with the Hypoxia-Activated Prodrug CP-506.

Mol Cancer Ther 2021 Oct 8. Epub 2021 Oct 8.

Maastricht University Medical Centre.

Hypoxia-activated prodrugs (HAPs) are a promising class of antineoplastic agents that can selectively eliminate hypoxic tumor cells. The present study evaluates the hypoxia-selectivity and antitumor activity of CP-506, a DNA alkylating HAP with favorable pharmacological properties. Stoichiometry of reduction, one-electron affinity, and back-oxidation rate of CP-506 were characterized by fast-reaction radiolytic methods with observed parameters fulfilling requirements for oxygen-sensitive bioactivation. Net reduction, metabolism, and cytotoxicity of CP-506 were maximally inhibited at oxygen concentrations above 1 μM (0.1% O2). CP-506 demonstrated cytotoxicity selectively in hypoxic 2D and 3D cell cultures with normoxic/anoxic IC50 ratios up to 203. Complete resistance to aerobic (two-electron) metabolism by aldo-keto reductase 1C3 was confirmed through gain-of-function studies whilst retention of hypoxic (one-electron) bioactivation by various diflavin oxidoreductases was also demonstrated. In vivo, the antitumor effects of CP-506 were selective for hypoxic tumor cells and causally related to tumor oxygenation. CP-506 effectively decreased the hypoxic fraction and inhibited growth of a wide range of hypoxic xenografts. A multivariate regression analysis revealed baseline tumor hypoxia and in vitro sensitivity to CP-506 to significantly correlate with treatment response. Our results demonstrate that CP-506 selectively targets hypoxic tumor cells and has broad antitumor activity. Our data indicates that tumor hypoxia and cellular sensitivity to CP-506 are strong determinants of the antitumor effects of CP-506.
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http://dx.doi.org/10.1158/1535-7163.MCT-21-0406DOI Listing
October 2021

Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods.

J Pers Med 2021 Aug 27;11(9). Epub 2021 Aug 27.

The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands.

Radiomics converts medical images into mineable data via a high-throughput extraction of quantitative features used for clinical decision support. However, these radiomic features are susceptible to variation across scanners, acquisition protocols, and reconstruction settings. Various investigations have assessed the reproducibility and validation of radiomic features across these discrepancies. In this narrative review, we combine systematic keyword searches with prior domain knowledge to discuss various harmonization solutions to make the radiomic features more reproducible across various scanners and protocol settings. Different harmonization solutions are discussed and divided into two main categories: image domain and feature domain. The image domain category comprises methods such as the standardization of image acquisition, post-processing of raw sensor-level image data, data augmentation techniques, and style transfer. The feature domain category consists of methods such as the identification of reproducible features and normalization techniques such as statistical normalization, intensity harmonization, ComBat and its derivatives, and normalization using deep learning. We also reflect upon the importance of deep learning solutions for addressing variability across multi-centric radiomic studies especially using generative adversarial networks (GANs), neural style transfer (NST) techniques, or a combination of both. We cover a broader range of methods especially GANs and NST methods in more detail than previous reviews.
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http://dx.doi.org/10.3390/jpm11090842DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8472571PMC
August 2021

Reproducibility of CT-Based Hepatocellular Carcinoma Radiomic Features across Different Contrast Imaging Phases: A Proof of Concept on SORAMIC Trial Data.

Cancers (Basel) 2021 Sep 16;13(18). Epub 2021 Sep 16.

The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6200 MD Maastricht, The Netherlands.

Handcrafted radiomic features (HRFs) are quantitative imaging features extracted from regions of interest on medical images which can be correlated with clinical outcomes and biologic characteristics. While HRFs have been used to train predictive and prognostic models, their reproducibility has been reported to be affected by variations in scan acquisition and reconstruction parameters, even within the same imaging vendor. In this work, we evaluated the reproducibility of HRFs across the arterial and portal venous phases of contrast-enhanced computed tomography images depicting hepatocellular carcinomas, as well as the potential of ComBat harmonization to correct for this difference. ComBat harmonization is a method based on Bayesian estimates that was developed for gene expression arrays, and has been investigated as a potential method for harmonizing HRFs. Our results show that the majority of HRFs are not reproducible between the arterial and portal venous imaging phases, yet a number of HRFs could be used interchangeably between those phases. Furthermore, ComBat harmonization increased the number of reproducible HRFs across both phases by 1%. Our results guide the pooling of arterial and venous phases from different patients in an effort to increase cohort size, as well as joint analysis of the phases.
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http://dx.doi.org/10.3390/cancers13184638DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8468150PMC
September 2021

MRI-based delta-radiomics predicts pathologic complete response in high-grade soft-tissue sarcoma patients treated with neoadjuvant therapy.

Radiother Oncol 2021 Oct 4;164:73-82. Epub 2021 Oct 4.

Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum, München, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Germany.

Purpose: In high-grade soft-tissue sarcomas (STS) the standard of care encompasses multimodal therapy regimens. While there is a growing body of evidence for prognostic pretreatment radiomic models, we hypothesized that temporal changes in radiomic features following neoadjuvant treatment ("delta-radiomics") may be able to predict the pathological complete response (pCR).

Methods: MRI scans (T1-weighted with fat-saturation and contrast-enhancement (T1FSGd) and T2-weighted with fat-saturation (T2FS)) of patients with STS of the extremities and trunk treated with neoadjuvant therapy were gathered from two independent institutions (training: 103, external testing: 53 patients). pCR was defined as <5% viable cells. After segmentation and preprocessing, 105 radiomic features were extracted. Delta-radiomic features were calculated by subtraction of features derived from MRI scans obtained before and after neoadjuvant therapy. After feature reduction, machine learning modeling was performed in 100 iterations of 3-fold nested cross-validation. Delta-radiomic models were compared with single timepoint models in the testing cohort.

Results: The combined delta-radiomic models achieved the best area under the receiver operating characteristic curve (AUC) of 0.75. Pre-therapeutic tumor volume was the best conventional predictor (AUC 0.70). The T2FS-based delta-radiomic model had the most balanced classification performance with a balanced accuracy of 0.69. Delta-radiomic models achieved better reproducibility than single timepoint radiomic models, RECIST or the peri-therapeutic volume change. Delta-radiomic models were significantly associated with survival in multivariate Cox regression.

Conclusion: This exploratory analysis demonstrated that MRI-based delta-radiomics improves prediction of pCR over tumor volume and RECIST. Delta-radiomics may one day function as a biomarker for personalized treatment adaptations.
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http://dx.doi.org/10.1016/j.radonc.2021.08.023DOI Listing
October 2021

Exploratory Radiomic Analysis of Conventional vs. Quantitative Brain MRI: Toward Automatic Diagnosis of Early Multiple Sclerosis.

Front Neurosci 2021 5;15:679941. Epub 2021 Aug 5.

GIGA Cyclotron Research Centre In Vivo Imaging, University of Liège, Liège, Belgium.

Conventional magnetic resonance imaging (cMRI) is poorly sensitive to pathological changes related to multiple sclerosis (MS) in normal-appearing white matter (NAWM) and gray matter (GM), with the added difficulty of not being very reproducible. Quantitative MRI (qMRI), on the other hand, attempts to represent the physical properties of tissues, making it an ideal candidate for quantitative medical image analysis or radiomics. We therefore hypothesized that qMRI-based radiomic features have added diagnostic value in MS compared to cMRI. This study investigated the ability of cMRI (T1w) and qMRI features extracted from white matter (WM), NAWM, and GM to distinguish between MS patients (MSP) and healthy control subjects (HCS). We developed exploratory radiomic classification models on a dataset comprising 36 MSP and 36 HCS recruited in CHU Liege, Belgium, acquired with cMRI and qMRI. For each image type and region of interest, qMRI radiomic models for MS diagnosis were developed on a training subset and validated on a testing subset. Radiomic models based on cMRI were developed on the entire training dataset and externally validated on open-source datasets with 167 HCS and 10 MSP. Ranked by region of interest, the best diagnostic performance was achieved in the whole WM. Here the model based on magnetization transfer imaging (a type of qMRI) features yielded a median area under the receiver operating characteristic curve (AUC) of 1.00 in the testing sub-cohort. Ranked by image type, the best performance was achieved by the magnetization transfer models, with median AUCs of 0.79 (0.69-0.90, 90% CI) in NAWM and 0.81 (0.71-0.90) in GM. The external validation of the T1w models yielded an AUC of 0.78 (0.47-1.00) in the whole WM, demonstrating a large 95% CI and a low sensitivity of 0.30 (0.10-0.70). This exploratory study indicates that qMRI radiomics could provide efficient diagnostic information using NAWM and GM analysis in MSP. T1w radiomics could be useful for a fast and automated check of conventional MRI for WM abnormalities once acquisition and reconstruction heterogeneities have been overcome. Further prospective validation is needed, involving more data for better interpretation and generalization of the results.
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http://dx.doi.org/10.3389/fnins.2021.679941DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8374240PMC
August 2021

Privacy preserving distributed learning classifiers - Sequential learning with small sets of data.

Comput Biol Med 2021 09 31;136:104716. Epub 2021 Jul 31.

The D-Lab, Department of Precision Medicine, GROW - School for Oncology, Maastricht University, Maastricht, the Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, the Netherlands.

Background: Artificial intelligence (AI) typically requires a significant amount of high-quality data to build reliable models, where gathering enough data within a single institution can be particularly challenging. In this study we investigated the impact of using sequential learning to exploit very small, siloed sets of clinical and imaging data to train AI models. Furthermore, we evaluated the capacity of such models to achieve equivalent performance when compared to models trained with the same data over a single centralized database.

Methods: We propose a privacy preserving distributed learning framework, learning sequentially from each dataset. The framework is applied to three machine learning algorithms: Logistic Regression, Support Vector Machines (SVM), and Perceptron. The models were evaluated using four open-source datasets (Breast cancer, Indian liver, NSCLC-Radiomics dataset, and Stage III NSCLC).

Findings: The proposed framework ensured a comparable predictive performance against a centralized learning approach. Pairwise DeLong tests showed no significant difference between the compared pairs for each dataset.

Interpretation: Distributed learning contributes to preserve medical data privacy. We foresee this technology will increase the number of collaborative opportunities to develop robust AI, becoming the default solution in scenarios where collecting enough data from a single reliable source is logistically impossible. Distributed sequential learning provides privacy persevering means for institutions with small but clinically valuable datasets to collaboratively train predictive AI while preserving the privacy of their patients. Such models perform similarly to models that are built on a larger central dataset.
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http://dx.doi.org/10.1016/j.compbiomed.2021.104716DOI Listing
September 2021

A review in radiomics: Making personalized medicine a reality via routine imaging.

Med Res Rev 2021 Jul 26. Epub 2021 Jul 26.

Radiomics (Oncoradiomics SA), Liège, Belgium.

Radiomics is the quantitative analysis of standard-of-care medical imaging; the information obtained can be applied within clinical decision support systems to create diagnostic, prognostic, and/or predictive models. Radiomics analysis can be performed by extracting hand-crafted radiomics features or via deep learning algorithms. Radiomics has evolved tremendously in the last decade, becoming a bridge between imaging and precision medicine. Radiomics exploits sophisticated image analysis tools coupled with statistical elaboration to extract the wealth of information hidden inside medical images, such as computed tomography (CT), magnetic resonance (MR), and/or Positron emission tomography (PET) scans, routinely performed in the everyday clinical practice. Many efforts have been devoted in recent years to the standardization and validation of radiomics approaches, to demonstrate their usefulness and robustness beyond any reasonable doubts. However, the booming of publications and commercial applications of radiomics approaches warrant caution and proper understanding of all the factors involved to avoid "scientific pollution" and overly enthusiastic claims by researchers and clinicians alike. For these reasons the present review aims to be a guidebook of sorts, describing the process of radiomics, its pitfalls, challenges, and opportunities, along with its ability to improve clinical decision-making, from oncology and respiratory medicine to pharmacological and genotyping studies.
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http://dx.doi.org/10.1002/med.21846DOI Listing
July 2021

Hypoxia-activated prodrug derivatives of anti-cancer drugs: a patent review 2006 - 2021.

Expert Opin Ther Pat 2021 Jul 28:1-12. Epub 2021 Jul 28.

IBMM, Univ Montpellier, CNRS, ENSCM, Montpellier, France.

Introduction: The hypoxic tumor microenvironment represents a persistent obstacle in the treatment of most solid tumors. In the past years, significant efforts have been made to improve the efficacy of anti-cancer drugs. Therefore, hypoxia-activated prodrugs (HAPs) of chemotherapeutic compounds have attracted widespread interest as a therapeutic means to treat hypoxic tumors.

Areas Covered: This updated review paper covers key patents published between 2006 and 2021 on the developments of HAP derivatives of anti-cancer compounds.

Expert Opinion: Despite significant achievements in the development of HAP derivatives of anti-cancer compounds and although many clinical trials have been performed or are ongoing both as monotherapies and as part of combination therapies, there has currently no HAP anti-cancer agent been commercialized into the market. Unsuccessful clinical translation is partly due to the lack of patient stratification based on reliable biomarkers that are predictive of a positive response to hypoxia-targeted therapy.
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http://dx.doi.org/10.1080/13543776.2021.1954617DOI Listing
July 2021

A Prospectively Validated Prognostic Model for Patients with Locally Advanced Squamous Cell Carcinoma of the Head and Neck Based on Radiomics of Computed Tomography Images.

Cancers (Basel) 2021 Jun 29;13(13). Epub 2021 Jun 29.

The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands.

Background: Locoregionally advanced head and neck squamous cell carcinoma (HNSCC) patients have high relapse and mortality rates. Imaging-based decision support may improve outcomes by optimising personalised treatment, and support patient risk stratification. We propose a multifactorial prognostic model including radiomics features to improve risk stratification for advanced HNSCC, compared to TNM eighth edition, the gold standard.

Patient And Methods: Data of 666 retrospective- and 143 prospective-stage III-IVA/B HNSCC patients were collected. A multivariable Cox proportional-hazards model was trained to predict overall survival (OS) using diagnostic CT-based radiomics features extracted from the primary tumour. Separate analyses were performed using TNM8, tumour volume, clinical and biological variables, and combinations thereof with radiomics features. Patient risk stratification in three groups was assessed through Kaplan-Meier (KM) curves. A log-rank test was performed for significance (-value < 0.05). The prognostic accuracy was reported through the concordance index (CI).

Results: A model combining an 11-feature radiomics signature, clinical and biological variables, TNM8, and volume could significantly stratify the validation cohort into three risk groups ( < 0∙01, CI of 0.79 as validation).

Conclusion: A combination of radiomics features with other predictors can predict OS very accurately for advanced HNSCC patients and improves on the current gold standard of TNM8.
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http://dx.doi.org/10.3390/cancers13133271DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8269129PMC
June 2021

Radiomics in Lung Diseases Imaging: State-of-the-Art for Clinicians.

J Pers Med 2021 Jun 25;11(7). Epub 2021 Jun 25.

Department of Respiratory Medicine, University Hospital of Liège, 4000 Liège, Belgium.

Artificial intelligence (AI) has increasingly been serving the field of radiology over the last 50 years. As modern medicine is evolving towards precision medicine, offering personalized patient care and treatment, the requirement for robust imaging biomarkers has gradually increased. Radiomics, a specific method generating high-throughput extraction of a tremendous amount of quantitative imaging data using data-characterization algorithms, has shown great potential in individuating imaging biomarkers. Radiomic analysis can be implemented through the following two methods: hand-crafted radiomic features extraction or deep learning algorithm. Its application in lung diseases can be used in clinical decision support systems, regarding its ability to develop descriptive and predictive models in many respiratory pathologies. The aim of this article is to review the recent literature on the topic, and briefly summarize the interest of radiomics in chest Computed Tomography (CT) and its pertinence in the field of pulmonary diseases, from a clinician's perspective.
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http://dx.doi.org/10.3390/jpm11070602DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8306026PMC
June 2021

Development and External Validation of Deep-Learning-Based Tumor Grading Models in Soft-Tissue Sarcoma Patients Using MR Imaging.

Cancers (Basel) 2021 Jun 8;13(12). Epub 2021 Jun 8.

Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany.

Background: In patients with soft-tissue sarcomas, tumor grading constitutes a decisive factor to determine the best treatment decision. Tumor grading is obtained by pathological work-up after focal biopsies. Deep learning (DL)-based imaging analysis may pose an alternative way to characterize STS tissue. In this work, we sought to non-invasively differentiate tumor grading into low-grade (G1) and high-grade (G2/G3) STS using DL techniques based on MR-imaging.

Methods: Contrast-enhanced T1-weighted fat-saturated (T1FSGd) MRI sequences and fat-saturated T2-weighted (T2FS) sequences were collected from two independent retrospective cohorts (training: 148 patients, testing: 158 patients). Tumor grading was determined following the French Federation of Cancer Centers Sarcoma Group in pre-therapeutic biopsies. DL models were developed using transfer learning based on the DenseNet 161 architecture.

Results: The T1FSGd and T2FS-based DL models achieved area under the receiver operator characteristic curve (AUC) values of 0.75 and 0.76 on the test cohort, respectively. T1FSGd achieved the best F1-score of all models (0.90). The T2FS-based DL model was able to significantly risk-stratify for overall survival. Attention maps revealed relevant features within the tumor volume and in border regions.

Conclusions: MRI-based DL models are capable of predicting tumor grading with good reproducibility in external validation.
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http://dx.doi.org/10.3390/cancers13122866DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8227009PMC
June 2021

Efficient Secretion of Murine IL-2 From an Attenuated Strain of , a Novel Delivery Vehicle for Cancer Immunotherapy.

Front Microbiol 2021 8;12:669488. Epub 2021 Jun 8.

The M-Lab, Department of Precision Medicine, GROW - School of Oncology, Maastricht University, Maastricht, Netherlands.

Despite a history dating back to the 1800s, using bacteria to treat cancer has not advanced beyond the observation that they can colonise and partially destroy solid tumours. Progress has been hampered by their inability to eradicate the viable portion of tumours, and an instinctive anxiety around injecting patients with a bacterium whose close relatives cause tetanus and botulism. However, recent advances in techniques to genetically engineer species gives cause to revisit this concept. This paper illustrates these developments through the attenuation of to enhance its clinical safety, and through the expression and secretion of an immunotherapeutic. An 8.6 kb sequence, corresponding to a haemolysin operon, was deleted from the genome and replaced with a short non-coding sequence. The resultant phenotype of this strain, named -NT, showed a reduction of haemolysis to levels similar to the probiotic strain, M588. Comparison to the parental strain showed no change in growth or sporulation. Following injection of tumour-bearing mice with purified spores of the attenuated strain, high levels of germination were detected in all tumours. Very low levels of spores and vegetative cells were detected in the spleen and lymph nodes. The new strain was transformed with four different murine IL-2-expressing plasmids, differentiated by promoter and signal peptide sequences. Biologically active mIL-2, recovered from the extracellular fraction of bacterial cultures, was shown to stimulate proliferation of T cells. With this investigation we propose a new, safer candidate for intratumoral delivery of cancer immunotherapeutics.
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http://dx.doi.org/10.3389/fmicb.2021.669488DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8217651PMC
June 2021

Development of a Management Algorithm for Acute and Chronic Radiation Urethritis and Cystitis.

Urol Int 2021 Jun 15:1-12. Epub 2021 Jun 15.

Department of Urology, St. Antonius Hospital, Gronau, Germany.

Objective: The purpose of this review was to summarize the current literature on the assessment and treatment of radiation urethritis and cystitis (RUC) for the development of an evidenced-based management algorithm.

Material And Methods: The PubMed/MEDLINE database was searched by a multidisciplinary group of experts in January 2021.

Results: In total, 48 publications were identified. Three different types of RUC can be observed in clinical practice: inflammation-predominant, bleeding-predominant, and the combination of inflammation- and bleeding-RUC. There is no consensus on the optimal treatment of RUC. Inflammation-predominant RUC should be treated symptomatically based on the existence of bothersome storage or voiding lower urinary tract symptom as well as on pain. When bleeding-predominant RUC has occurred, hydration and hyperbaric oxygen therapy (HOT) should be used first and, if HOT is not available, oral drugs instead (sodium pentosane polysulfate, aminocaproic acid, immunokine WF 10, conjugated estrogene, or pentoxifylline + vitamin E). If local bleeding persists, focal therapy of bleeding vessels with a laser or electrocoagulation is indicated. In case of generalized bleeding, intravesical installation should be initiated (formalin, aluminium salts, and hyaluronic acid/chondroitin). Vessel embolization is a less invasive treatment with potentially less complications and good clinical outcomes. Open- or robot-assisted surgery is indicated in patients with permanent, life-threatening bleeding, or fistulae.

Conclusions: Treatment of RUC, if not self-limiting, should be done according to the type of RUC and in a stepwise approach. Conservative/medical treatment (oral and topic agents) should primarily be used before invasive (transurethral) treatments.
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http://dx.doi.org/10.1159/000515716DOI Listing
June 2021

Modeling-Based Decision Support System for Radical Prostatectomy Versus External Beam Radiotherapy for Prostate Cancer Incorporating an Clinical Trial and a Cost-Utility Study.

Cancers (Basel) 2021 May 29;13(11). Epub 2021 May 29.

The D-Lab, Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands.

The aim of this study is to build a decision support system (DSS) to select radical prostatectomy (RP) or external beam radiotherapy (EBRT) for low- to intermediate-risk prostate cancer patients. We used an individual state-transition model based on predictive models for estimating tumor control and toxicity probabilities. We performed analyses on a synthetically generated dataset of 1000 patients with realistic clinical parameters, externally validated by comparison to randomized clinical trials, and set up an in silico clinical trial for elderly patients. We assessed the cost-effectiveness (CE) of the DSS for treatment selection by comparing it to randomized treatment allotment. Using the DSS, 47.8% of synthetic patients were selected for RP and 52.2% for EBRT. During validation, differences with the simulations of late toxicity and biochemical failure never exceeded 2%. The in silico trial showed that for elderly patients, toxicity has more influence on the decision than TCP, and the predicted QoL depends on the initial erectile function. The DSS is estimated to result in cost savings (EUR 323 (95% CI: EUR 213-433)) and more quality-adjusted life years (QALYs; 0.11 years, 95% CI: 0.00-0.22) than randomized treatment selection.
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http://dx.doi.org/10.3390/cancers13112687DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8198879PMC
May 2021

MRI-Based Radiomics Analysis for the Pretreatment Prediction of Pathologic Complete Tumor Response to Neoadjuvant Systemic Therapy in Breast Cancer Patients: A Multicenter Study.

Cancers (Basel) 2021 May 18;13(10). Epub 2021 May 18.

Department of Surgery, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands.

This retrospective study investigated the value of pretreatment contrast-enhanced Magnetic Resonance Imaging (MRI)-based radiomics for the prediction of pathologic complete tumor response to neoadjuvant systemic therapy in breast cancer patients. A total of 292 breast cancer patients, with 320 tumors, who were treated with neo-adjuvant systemic therapy and underwent a pretreatment MRI exam were enrolled. As the data were collected in two different hospitals with five different MRI scanners and varying acquisition protocols, three different strategies to split training and validation datasets were used. Radiomics, clinical, and combined models were developed using random forest classifiers in each strategy. The analysis of radiomics features had no added value in predicting pathologic complete tumor response to neoadjuvant systemic therapy in breast cancer patients compared with the clinical models, nor did the combined models perform significantly better than the clinical models. Further, the radiomics features selected for the models and their performance differed with and within the different strategies. Due to previous and current work, we tentatively attribute the lack of improvement in clinical models following the addition of radiomics to the effects of variations in acquisition and reconstruction parameters. The lack of reproducibility data (i.e., test-retest or similar) meant that this effect could not be analyzed. These results indicate the need for reproducibility studies to preselect reproducible features in order to properly assess the potential of radiomics.
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http://dx.doi.org/10.3390/cancers13102447DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8157878PMC
May 2021

Deciphering the glioblastoma phenotype by computed tomography radiomics.

Radiother Oncol 2021 07 10;160:132-139. Epub 2021 May 10.

Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands.

Introduction: Glioblastoma (GBM) is the most common malignant primary brain tumour which has, despite extensive treatment, a median overall survival of 15 months. Radiomics is the high-throughput extraction of large amounts of image features from radiographic images, which allows capturing the tumour phenotype in 3D and in a non-invasive way. In this study we assess the prognostic value of CT radiomics for overall survival in patients with a GBM.

Materials And Methods: Clinical data and pre-treatment CT images were obtained from 218 patients diagnosed with a GBM via biopsy who underwent radiotherapy +/- temozolomide between 2004 and 2015 treated at three independent institutes (n = 93, 62 and 63). A clinical prognostic score (CPS), a simple radiomics model consisting of volume based score (VPS), a complex radiomics prognostic score (RPS) and a combined clinical and radiomics (C + R)PS model were developed. The population was divided into three risk groups for each prognostic score and respective Kaplan-Meier curves were generated.

Results: Patient characteristics were broadly comparable. Clinically significant differences were observed with regards to radiation dose, tumour volume and performance status between datasets. Image acquisition parameters differed between institutes. The cross-validated c-indices were moderately discriminative and for the CPS ranged from 0.63 to 0.65; the VPS c-indices ranged between 0.52 and 0.61; the RPS c-indices ranged from 0.57 to 0.64 and the combined clinical and radiomics model resulted in c-indices of 0.59-0.71.

Conclusion: In this study clinical and CT radiomics features were used to predict OS in GBM. Discrimination between low-, middle- and high-risk patients based on the combined clinical and radiomics model was comparable to previous MRI-based models.
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http://dx.doi.org/10.1016/j.radonc.2021.05.002DOI Listing
July 2021

The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset.

PLoS One 2021 7;16(5):e0251147. Epub 2021 May 7.

The D-Lab, Department of Precision Medicine, GROW- School for Oncology, Maastricht University, Maastricht, The Netherlands.

Radiomics-the high throughput extraction of quantitative features from medical images and their correlation with clinical and biological endpoints- is the subject of active and extensive research. Although the field shows promise, the generalizability of radiomic signatures is affected significantly by differences in scan acquisition and reconstruction settings. Previous studies reported on the sensitivity of radiomic features (RFs) to test-retest variability, inter-observer segmentation variability, and intra-scanner variability. A framework involving robust radiomics analysis and the application of a post-reconstruction feature harmonization method using ComBat was recently proposed to address these challenges. In this study, we investigated the reproducibility of RFs across different scanners and scanning parameters using this framework. We analysed thirteen scans of a ten-layer phantom that were acquired differently. Each layer was subdivided into sixteen regions of interest (ROIs), and the scans were compared in a pairwise manner, resulting in seventy-eight different scenarios. Ninety-one RFs were extracted from each ROI. As hypothesized, we demonstrate that the reproducibility of a given RF is not a constant but is dependent on the heterogeneity found in the data under analysis. The number (%) of reproducible RFs varied across the pairwise scenarios investigated, having a wide range between 8 (8.8%) and 78 (85.7%) RFs. Furthermore, in contrast to what has been previously reported, and as hypothesized in the robust radiomics analysis framework, our results demonstrate that ComBat cannot be applied to all RFs but rather on a percentage of those-the "ComBatable" RFs-which differed depending on the data being harmonized. The number (%) of reproducible RFs following ComBat harmonization varied across the pairwise scenarios investigated, ranging from 14 (15.4%) to 80 (87.9%) RFs, and was found to depend on the heterogeneity in the data. We conclude that the standardization of image acquisition protocols remains the cornerstone for improving the reproducibility of RFs, and the generalizability of the signatures developed. Our proposed approach helps identify the reproducible RFs across different datasets.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0251147PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8104396PMC
October 2021

The Effects of In-Plane Spatial Resolution on CT-Based Radiomic Features' Stability with and without ComBat Harmonization.

Cancers (Basel) 2021 Apr 13;13(8). Epub 2021 Apr 13.

Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.

While handcrafted radiomic features (HRFs) have shown promise in the field of personalized medicine, many hurdles hinder its incorporation into clinical practice, including but not limited to their sensitivity to differences in acquisition and reconstruction parameters. In this study, we evaluated the effects of differences in in-plane spatial resolution (IPR) on HRFs, using a phantom dataset (n = 14) acquired on two scanner models. Furthermore, we assessed the effects of interpolation methods (IMs), the choice of a new unified in-plane resolution (NUIR), and ComBat harmonization on the reproducibility of HRFs. The reproducibility of HRFs was significantly affected by variations in IPR, with pairwise concordant HRFs, as measured by the concordance correlation coefficient (CCC), ranging from 42% to 95%. The number of concordant HRFs (CCC > 0.9) after resampling varied depending on (i) the scanner model, (ii) the IM, and (iii) the NUIR. The number of concordant HRFs after ComBat harmonization depended on the variations between the batches harmonized. The majority of IMs resulted in a higher number of concordant HRFs compared to ComBat harmonization, and the combination of IMs and ComBat harmonization did not yield a significant benefit. Our developed framework can be used to assess the reproducibility and harmonizability of RFs.
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http://dx.doi.org/10.3390/cancers13081848DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8103509PMC
April 2021

Prognostic Assessment in High-Grade Soft-Tissue Sarcoma Patients: A Comparison of Semantic Image Analysis and Radiomics.

Cancers (Basel) 2021 Apr 16;13(8). Epub 2021 Apr 16.

Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany.

Background: In patients with soft-tissue sarcomas of the extremities, the treatment decision is currently regularly based on tumor grading and size. The imaging-based analysis may pose an alternative way to stratify patients' risk. In this work, we compared the value of MRI-based radiomics with expert-derived semantic imaging features for the prediction of overall survival (OS).

Methods: Fat-saturated T2-weighted sequences (T2FS) and contrast-enhanced T1-weighted fat-saturated (T1FSGd) sequences were collected from two independent retrospective cohorts (training: 108 patients; testing: 71 patients). After preprocessing, 105 radiomic features were extracted. Semantic imaging features were determined by three independent radiologists. Three machine learning techniques (elastic net regression (ENR), least absolute shrinkage and selection operator, and random survival forest) were compared to predict OS.

Results: ENR models achieved the best predictive performance. Histologies and clinical staging differed significantly between both cohorts. The semantic prognostic model achieved a predictive performance with a C-index of 0.58 within the test set. This was worse compared to a clinical staging system (C-index: 0.61) and the radiomic models (C-indices: T1FSGd: 0.64, T2FS: 0.63). Both radiomic models achieved significant patient stratification.

Conclusions: T2FS and T1FSGd-based radiomic models outperformed semantic imaging features for prognostic assessment.
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http://dx.doi.org/10.3390/cancers13081929DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8073388PMC
April 2021

Knowledge Graphs for COVID-19: An Exploratory Review of the Current Landscape.

J Pers Med 2021 Apr 14;11(4). Epub 2021 Apr 14.

The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6229 ER Maastricht, The Netherlands.

Background: Searching through the COVID-19 research literature to gain actionable clinical insight is a formidable task, even for experts. The usefulness of this corpus in terms of improving patient care is tied to the ability to see the big picture that emerges when the studies are seen in conjunction rather than in isolation. When the answer to a search query requires linking together multiple pieces of information across documents, simple keyword searches are insufficient. To answer such complex information needs, an innovative artificial intelligence (AI) technology named a knowledge graph (KG) could prove to be effective.

Methods: We conducted an exploratory literature review of KG applications in the context of COVID-19. The search term used was "covid-19 knowledge graph". In addition to PubMed, the first five pages of search results for Google Scholar and Google were considered for inclusion. Google Scholar was used to include non-peer-reviewed or non-indexed articles such as pre-prints and conference proceedings. Google was used to identify companies or consortiums active in this domain that have not published any literature, peer-reviewed or otherwise.

Results: Our search yielded 34 results on PubMed and 50 results each on Google and Google Scholar. We found KGs being used for facilitating literature search, drug repurposing, clinical trial mapping, and risk factor analysis.

Conclusions: Our synopses of these works make a compelling case for the utility of this nascent field of research.
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http://dx.doi.org/10.3390/jpm11040300DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8070774PMC
April 2021

Can predicting COVID-19 mortality in a European cohort using only demographic and comorbidity data surpass age-based prediction: An externally validated study.

PLoS One 2021 15;16(4):e0249920. Epub 2021 Apr 15.

The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands.

Objective: To establish whether one can build a mortality prediction model for COVID-19 patients based solely on demographics and comorbidity data that outperforms age alone. Such a model could be a precursor to implementing smart lockdowns and vaccine distribution strategies.

Methods: The training cohort comprised 2337 COVID-19 inpatients from nine hospitals in The Netherlands. The clinical outcome was death within 21 days of being discharged. The features were derived from electronic health records collected during admission. Three feature selection methods were used: LASSO, univariate using a novel metric, and pairwise (age being half of each pair). 478 patients from Belgium were used to test the model. All modeling attempts were compared against an age-only model.

Results: In the training cohort, the mortality group's median age was 77 years (interquartile range = 70-83), higher than the non-mortality group (median = 65, IQR = 55-75). The incidence of former/active smokers, male gender, hypertension, diabetes, dementia, cancer, chronic obstructive pulmonary disease, chronic cardiac disease, chronic neurological disease, and chronic kidney disease was higher in the mortality group. All stated differences were statistically significant after Bonferroni correction. LASSO selected eight features, novel univariate chose five, and pairwise chose none. No model was able to surpass an age-only model in the external validation set, where age had an AUC of 0.85 and a balanced accuracy of 0.77.

Conclusion: When applied to an external validation set, we found that an age-only mortality model outperformed all modeling attempts (curated on www.covid19risk.ai) using three feature selection methods on 22 demographic and comorbid features.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0249920PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8049248PMC
April 2021

Development and external validation of a non-invasive molecular status predictor of chromosome 1p/19q co-deletion based on MRI radiomics analysis of Low Grade Glioma patients.

Eur J Radiol 2021 Jun 5;139:109678. Epub 2021 Apr 5.

The D-Lab, Department of Precision Medicine, GROW- School for Oncology, Maastricht University Maastricht, the Netherlands; Department of Radiology and Nuclear Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, Maastricht, the Netherlands.

Purpose: The 1p/19q co-deletion status has been demonstrated to be a prognostic biomarker in lower grade glioma (LGG). The objective of this study was to build a magnetic resonance (MRI)-derived radiomics model to predict the 1p/19q co-deletion status.

Method: 209 pathology-confirmed LGG patients from 2 different datasets from The Cancer Imaging Archive were retrospectively reviewed; one dataset with 159 patients as the training and discovery dataset and the other one with 50 patients as validation dataset. Radiomics features were extracted from T2- and T1-weighted post-contrast MRI resampled data using linear and cubic interpolation methods. For each of the voxel resampling methods a three-step approach was used for feature selection and a random forest (RF) classifier was trained on the training dataset. Model performance was evaluated on training and validation datasets and clinical utility indexes (CUIs) were computed. The distributions and intercorrelation for selected features were analyzed.

Results: Seven radiomics features were selected from the cubic interpolated features and five from the linear interpolated features on the training dataset. The RF classifier showed similar performance for cubic and linear interpolation methods in the training dataset with accuracies of 0.81 (0.75-0.86) and 0.76 (0.71-0.82) respectively; in the validation dataset the accuracy dropped to 0.72 (0.6-0.82) using cubic interpolation and 0.72 (0.6-0.84) using linear resampling. CUIs showed the model achieved satisfactory negative values (0.605 using cubic interpolation and 0.569 for linear interpolation).

Conclusions: MRI has the potential for predicting the 1p/19q status in LGGs. Both cubic and linear interpolation methods showed similar performance in external validation.
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http://dx.doi.org/10.1016/j.ejrad.2021.109678DOI Listing
June 2021

Charged Particle and Conventional Radiotherapy: Current Implications as Partner for Immunotherapy.

Cancers (Basel) 2021 Mar 23;13(6). Epub 2021 Mar 23.

The M-Lab, Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands.

Radiotherapy (RT) has been shown to interfere with inflammatory signals and to enhance tumor immunogenicity via, e.g., immunogenic cell death, thereby potentially augmenting the therapeutic efficacy of immunotherapy. Conventional RT consists predominantly of high energy photon beams. Hypofractionated RT regimens administered, e.g., by stereotactic body radiation therapy (SBRT), are increasingly investigated in combination with cancer immunotherapy within clinical trials. Despite intensive preclinical studies, the optimal dose per fraction and dose schemes for elaboration of RT induced immunogenic potential remain inconclusive. Compared to the scenario of combined immune checkpoint inhibition (ICI) and RT, multimodal therapies utilizing other immunotherapy principles such as adoptive transfer of immune cells, vaccination strategies, targeted immune-cytokines and agonists are underrepresented in both preclinical and clinical settings. Despite the clinical success of ICI and RT combination, e.g., prolonging overall survival in locally advanced lung cancer, curative outcomes are still not achieved for most cancer entities studied. Charged particle RT (PRT) has gained interest as it may enhance tumor immunogenicity compared to conventional RT due to its unique biological and physical properties. However, whether PRT in combination with immune therapy will elicit superior antitumor effects both locally and systemically needs to be further investigated. In this review, the immunological effects of RT in the tumor microenvironment are summarized to understand their implications for immunotherapy combinations. Attention will be given to the various immunotherapeutic interventions that have been co-administered with RT so far. Furthermore, the theoretical basis and first evidences supporting a favorable immunogenicity profile of PRT will be examined.
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http://dx.doi.org/10.3390/cancers13061468DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8005048PMC
March 2021

[F]FDG PET radiomics to predict disease-free survival in cervical cancer: a multi-scanner/center study with external validation.

Eur J Nucl Med Mol Imaging 2021 10 26;48(11):3432-3443. Epub 2021 Mar 26.

GIGA-CRC in vivo Imaging, University of Liège, GIGA, Avenue de l'Hôpital 11, 4000, Liege, Belgium.

Purpose: To test the performances of native and tumour to liver ratio (TLR) radiomic features extracted from pre-treatment 2-[F] fluoro-2-deoxy-D-glucose ([F]FDG) PET/CT and combined with machine learning (ML) for predicting cancer recurrence in patients with locally advanced cervical cancer (LACC).

Methods: One hundred fifty-eight patients with LACC from multiple centers were retrospectively included in the study. Tumours were segmented using the Fuzzy Local Adaptive Bayesian (FLAB) algorithm. Radiomic features were extracted from the tumours and from regions drawn over the normal liver. Cox proportional hazard model was used to test statistical significance of clinical and radiomic features. Fivefold cross validation was used to tune the number of features. Seven different feature selection methods and four classifiers were tested. The models with the selected features were trained using bootstrapping and tested in data from each scanner independently. Reproducibility of radiomics features, clinical data added value and effect of ComBat-based harmonisation were evaluated across scanners.

Results: After a median follow-up of 23 months, 29% of the patients recurred. No individual radiomic or clinical features were significantly associated with cancer recurrence. The best model was obtained using 10 TLR features combined with clinical information. The area under the curve (AUC), F-score, precision and recall were respectively 0.78 (0.67-0.88), 0.49 (0.25-0.67), 0.42 (0.25-0.60) and 0.63 (0.20-0.80). ComBat did not improve the predictive performance of the best models. Both the TLR and the native models performance varied across scanners used in the test set.

Conclusion: [F]FDG PET radiomic features combined with ML add relevant information to the standard clinical parameters in terms of LACC patient's outcome but remain subject to variability across PET/CT devices.
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http://dx.doi.org/10.1007/s00259-021-05303-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8440288PMC
October 2021

Cycle-Consistent Generative Adversarial Network: Effect on Radiation Dose Reduction and Image Quality Improvement in Ultralow-Dose CT for Evaluation of Pulmonary Tuberculosis.

Korean J Radiol 2021 06 9;22(6):983-993. Epub 2021 Mar 9.

The D-Lab, Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.

Objective: To investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis.

Materials And Methods: Between June 2019 and November 2019, 103 patients (mean age, 40.8 ± 13.6 years; 61 men and 42 women) with pulmonary tuberculosis were prospectively enrolled to undergo standard-dose CT (120 kVp with automated exposure control), followed immediately by ULDCT (80 kVp and 10 mAs). The images of the two successive scans were used to train the CycleGAN framework for image-to-image translation. The denoising efficacy of the CycleGAN algorithm was compared with that of hybrid and model-based iterative reconstruction. Repeated-measures analysis of variance and Wilcoxon signed-rank test were performed to compare the objective measurements and the subjective image quality scores, respectively.

Results: With the optimized CycleGAN denoising model, using the ULDCT images as input, the peak signal-to-noise ratio and structural similarity index improved by 2.0 dB and 0.21, respectively. The CycleGAN-generated denoised ULDCT images typically provided satisfactory image quality for optimal visibility of anatomic structures and pathological findings, with a lower level of image noise (mean ± standard deviation [SD], 19.5 ± 3.0 Hounsfield unit [HU]) than that of the hybrid (66.3 ± 10.5 HU, < 0.001) and a similar noise level to model-based iterative reconstruction (19.6 ± 2.6 HU, > 0.908). The CycleGAN-generated images showed the highest contrast-to-noise ratios for the pulmonary lesions, followed by the model-based and hybrid iterative reconstruction. The mean effective radiation dose of ULDCT was 0.12 mSv with a mean 93.9% reduction compared to standard-dose CT.

Conclusion: The optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis.
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http://dx.doi.org/10.3348/kjr.2020.0988DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8154783PMC
June 2021

A Dutch phase III randomized multicenter trial: whole brain radiotherapy versus stereotactic radiotherapy for 4-10 brain metastases.

Neurooncol Adv 2021 Jan-Dec;3(1):vdab021. Epub 2021 Feb 1.

Department of Radiation Oncology, Haaglanden MC, Den Haag, the Netherlands.

Background: The clinical value of whole brain radiotherapy (WBRT) for brain metastases (BM) is a matter of debate due to the significant side effects involved. Stereotactic radiosurgery (SRS) is an attractive alternative treatment option that may avoid these side effects and improve local tumor control. We initiated a randomized trial (NCT02353000) to investigate whether quality of life is better preserved after SRS compared with WBRT in patients with multiple brain metastases.

Methods: Patients with 4-10 BM were randomized between the standard arm WBRT (total dose 20 Gy in 5 fractions) or SRS (single fraction or 3 fractions). The primary endpoint was the difference in quality of life (QOL) at 3 months post-treatment.

Results: The study was prematurely closed due to poor accrual. A total of 29 patients (13%) were randomized, of which 15 patients have been treated with SRS and 14 patients with WBRT. The median number of lesions were 6 (range: 4-9) and the median total treatment volume was 13.0 cc (range: 1.8-25.9 cc). QOL at 3 months decreased in the SRS group by 0.1 (SD = 0.2), compared to 0.2 (SD = 0.2) in the WBRT group ( = .23). The actuarial 1-year survival rates were 57% (SRS) and 31% (WBRT) ( = .52). The actuarial 1-year brain salvage-free survival rates were 50% (SRS) and 78% (WBRT) ( = .22).

Conclusion: In patients with 4-10 BM, SRS alone resulted in 1-year survival for 57% of patients while maintaining quality of life. Due to the premature closure of the trial, no statistically significant differences could be determined.
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http://dx.doi.org/10.1093/noajnl/vdab021DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7954103PMC
February 2021
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