Anne-Laure Boulesteix’s research while affiliated with Technische Universität München and other places


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Publications (39)


Stereotactic Radiosurgery versus Whole-Brain Radiotherapy in Patients with 4–10 Brain Metastases: A Nonrandomized Controlled Trial
  • Article

June 2023

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51 Reads

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14 Citations

Radiotherapy and Oncology

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Anna-Lena Kaempfel

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Anne-Laure Boulesteix

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[...]

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Background and purpose: There is no randomized evidence comparing whole-brain radiotherapy (WBRT) and stereotactic radiosurgery (SRS) in the treatment of multiple brain metastases. This prospective nonrandomized controlled single arm trial attempts to reduce the gap until prospective randomized controlled trial results are available. Material and methods: We included patients with 4-10 brain metastases and ECOG performance status ≤2 from all histologies except small-cell lung cancer, germ cell tumors, and lymphoma. The retrospective WBRT-cohort was selected 2:1 from consecutive patients treated within 2012-2017. Propensity-score matching was performed to adjust for confounding factors such as sex, age, primary tumor histology, dsGPA score, and systemic therapy. SRS was performed using a LINAC-based single-isocenter technique employing prescription doses from 15-20Gyx1 at the 80% isodose line. The historical control consisted of equivalent WBRT dose regimens of either 3Gyx10 or 2.5Gyx14. Results: Patients were recruited from 2017-2020, end of follow-up was July 1st, 2021. 40 patients were recruited to the SRS-cohort and 70 patients were eligible as controls in the WBRT-cohort. Median OS, and iPFS were 10.4months (95%-CI 9.3-NA) and 7.1months (95%-CI 3.9-14.2) for the SRS-cohort, and 6.5months (95%-CI 4.9-10.4), and 5.9months (95%-CI 4.1-8.8) for the WBRT-cohort, respectively. Differences were non-significant for OS (HR: 0.65; 95%-CI 0.40-1.05; P=.074) and iPFS (P=.28). No grade III toxicities were observed in the SRS-cohort. Conclusion: This trial did not meet its primary endpoint as the OS-improvement of SRS compared to WBRT was non-significant and thus superiority could not be proven. Prospective randomized trials in the era of immunotherapy and targeted therapies are warranted.



Figure 1. Examples of contrast clearance analysis (CCA). The images show four different patients (A-D), each with a regular contrast-enhanced T1-MRI sequence, a late phase T1-sequence w1 h after contrast media application, and their CCA (from left to right). Tumor tissue is depicted as blue in the CCA, while reactive tissue is depicted as red. (A) Glioblastoma (WHO 2016 grade IV) IDH wt: a frontoparietal lesion showing tumor tissue in a circular formation with reactive components centrally and at the lesional border (Patient ID 17). (B) Lung adenocarcinoma with brain metastases: a right cerebellar lesion showing tumor tissue with reactive components in the surrounding area (Patient ID 03). (C) Glioblastoma (WHO 2016 grade IV) IDH wt: a periventricular lesion showing spotted areas with reactive tissue (Patient ID 20). (D) Maxillary squamous cell cancer with brain infiltration: a lesion in the right temporal lobe consisting nearly entirely of reactive tissue (Patient ID 25). IDH, isocitrate dehydrogenase; MRI, magnetic resonance imaging; WHO, World Health Organization; wt, wild type.
MRI-based contrast clearance analysis shows high differentiation accuracy between radiation-induced reactions and progressive disease after cranial radiotherapy
  • Article
  • Full-text available

April 2022

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271 Reads

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11 Citations

ESMO Open

Background Pseudoprogression (PsP) or radiation necrosis (RN) may frequently occur after cranial radiotherapy and show a similar imaging pattern compared with progressive disease (PD). We aimed to evaluate the diagnostic accuracy of magnetic resonance imaging-based contrast clearance analysis (CCA) in this clinical setting. Patients and methods Patients with equivocal imaging findings after cranial radiotherapy were consecutively included into this monocentric prospective study. CCA was carried out by software-based automated subtraction of imaging features in late versus early T1-weighted sequences after contrast agent application. Two experienced neuroradiologists evaluated CCA with respect to PsP/RN and PD being blinded for histological findings. The radiological assessment was compared with the histopathological results, and its accuracy was calculated statistically. Results A total of 33 patients were included; 16 (48.5%) were treated because of a primary brain tumor (BT), and 17 (51.1%) because of a secondary BT. In one patient, CCA was technically infeasible. The accuracy of CCA in predicting the histological result was 0.84 [95% confidence interval (CI) 0.67-0.95; one-sided P = 0.051; n = 32]. Sensitivity and specificity of CCA were 0.93 (95% CI 0.66-1.00) and 0.78 (95% CI 0.52-0.94), respectively. The accuracy in patients with secondary BTs was 0.94 (95% CI 0.71-1.00) and nonsignificantly higher compared with patients with primary BT with an accuracy of 0.73 (95% CI 0.45-0.92), P = 0.16. Conclusions In this study, CCA was a highly accurate, easy, and helpful method for distinguishing PsP or RN from PD after cranial radiotherapy, especially in patients with secondary tumors after radiosurgical treatment.

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P05.01 Prospective validation trial of magnetic resonance imaging based Contrast Clearance Analysis (CCA) to differentiate between pseudoprogression/radiation necrosis and progressive disease following cranial radiotherapy

September 2021

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45 Reads

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1 Citation

Neuro-Oncology

BACKGROUND Pseudoprogression (PsP) or radiation necrosis (RN) may frequently occur after cranial radiotherapy and show a similar imaging pattern compared to progressive disease (PD). Even for experienced neuroradiologists, it remains challenging to distinguish between these clinically relevant disease states. We aimed to evaluate the diagnostic accuracy of magnetic resonance imaging (MRI) based Contrast Clearance Analysis (CCA) in this clinical setting. MATERIAL AND METHODS Patients with equivocal imaging findings after cranial radiotherapy were consecutively included into this monocentric prospective study. Assuming a true accuracy of 90% and setting the significance level to 0.05, N=33 patients are required to show that accuracy is larger than 70% with a power of 80% using a one-sided binomial test. CCA was performed by subtraction of imaging features in late vs early T1-weighted sequences after contrast-agent application. Two experienced neuroradiologists evaluated CCA with respect to PsP/RN and PD being blinded for FET PET and histological findings; histopathological diagnosis was based on stereotactic biopsy or resection for space-occupying processes. The radiological assessment was compared with the histopathological results, and its accuracy was calculated statistically. RESULTS Thirty-three patients were included; sixteen (48.5%) were treated because of a primary brain tumors, and 17 (51.1%) with brain metastases. In one patient, CCA was technically infeasible. The accuracy of CCA in predicting the histological result was 0.84 (95% CI 0.67–0.95; one-sided p=0.05; N=32). An accuracy of 0.85 (95% CI 0.68–0.95; one-sided p=0.04) would have been obtained in case of a correct classification in the non-analyzable case. Sensitivity and specificity of CCA were 0.93 (95%-CI 0.66–1.00) and 0.78 (95% CI 0.52–0.94), respectively. The accuracy in metastases patients was 0.94 (95% CI 0.71 - 1.00) and non-significantly higher compared to primary brain tumor patients with accuracy of 0.73 (95% CI 0.45 - 0.92), p=0.16. CONCLUSION In this study, CCA was a highly accurate, easy and helpful method for distinguishing PsP or RN from PD after cranial radiotherapy, especially in brain metastases patients after radiosurgical treatment.




Guest Editorial—Special Collection Topic: Statistical Systems Theory in Cancer Modeling, Diagnosis, and Therapy

March 2018

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45 Reads

Cancer Informatics

Aims and Scope Cancer is a systems disease involving mutations and altered regulation. This supplement treats cancer research as it pertains to 3 systems issues of an inherently statistical nature: regulatory modeling and information processing, diagnostic classification, and therapeutic intervention and control. Topics of interest include (but are not limited to) multiscale modeling, gene/protein transcriptional regulation, dynamical systems, pharmacokinetic/pharmacodynamic modeling, compensatory regulation, feedback, apoptotic and proliferative control, copy number-expression interaction, integration of different feature types, error estimation, and reproducibility. We are especially interested in how the above issues relate to the extremely high-dimensional data sets and small- to moderate-sized data sets typically involved in cancer research, for instance, their effect on statistical power, inference accuracy, and multiple comparisons.


Independent validation of a new reirradiation risk score (RRRS) for glioma patients predicting post-recurrence survival: A multicenter DKTK/ROG analysis

February 2018

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77 Reads

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43 Citations

Radiotherapy and Oncology

Background and purpose: Reirradiation (reRT) is a valid option with considerable efficacy in patients with recurrent high-grade glioma, but it is still not known which patients might be optimal candidates for a second course of irradiation. This study validated a newly developed prognostic score independently in an external patient cohort. Material and methods: The reRT risk score (RRRS) is based on a linear combination of initial histology, clinical performance status, and age derived from a multivariable model of 353 patients. This score can predict post-recurrence survival (PRS) after reRT. The validation dataset consisted of 212 patients. Results: The RRRS differentiates three prognostic groups. Discrimination and calibration were maintained in the validation group. Median PRS times in the development cohort for the good/intermediate/poor risk categories were 14.2, 9.1, and 5.3 months, respectively. The respective groups within the validation cohort displayed median PRS times of 13.8, 8.8, and 3.8 months, respectively. Uno's C for development data was 0.64 (CI: 0.60-0.69) and for validation data 0.63 (CI: 0.58-0.68). Conclusions: The RRRS has been successfully validated in an independent patient cohort. This linear combination of three easily determined clinicopathological factors allows for a reliable classification of patients and may be used as stratification factor for future trials.


Flow of included studies on disease prediction from serum metabolomics
Overview of reproducibility, readability and the clarity of the workflow pipeline of the overall data analysis in the studies reviewed. Green: detail is reported; red: detail is not reported; blue: counts of method/program/algorithm/performance metric used. *All code and data are available upon request but the definition of complete reproducibility is the availability of linked and executable code, so this study is not fully reproducible
Reporting of pretreatment steps employed in the studies reviewed. Green: detail is reported; red: detail is not reported; blue: counts of method/program/algorithm/performance metric used
Completeness of reporting of supervised analysis steps and counts of the algorithms, performance metrics and validation methods employed. Green: detail is reported; red: detail is not reported; blue: counts of method/program/algorithm/performance metric used
Critical review of reporting of the data analysis step in metabolomics

December 2017

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214 Reads

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68 Citations

Metabolomics

Introduction We present the first study to critically appraise the quality of reporting of the data analysis step in metabolomics studies since the publication of minimum reporting guidelines in 2007. Objectives The aim of this study was to assess the standard of reporting of the data analysis step in metabolomics biomarker discovery studies and to investigate whether the level of detail supplied allows basic understanding of the steps employed and/or reuse of the protocol. For the purposes of this review we define the data analysis step to include the data pretreatment step and the actual data analysis step, which covers algorithm selection, univariate analysis and multivariate analysis. Method We reviewed the literature to identify metabolomic studies of biomarker discovery that were published between January 2008 and December 2014. Studies were examined for completeness in reporting the various steps of the data pretreatment phase and data analysis phase and also for clarity of the workflow of these sections. Results We analysed 27 papers, published anytime in 2008 until the end of 2014 in the area or biomarker discovery in serum metabolomics. The results of this review showed that the data analysis step in metabolomics biomarker discovery studies is plagued by unclear and incomplete reporting. Major omissions and lack of logical flow render the data analysis’ workflows in these studies impossible to follow and therefore replicate or even imitate. Conclusions While we await the holy grail of computational reproducibility in data analysis to become standard, we propose that, at a minimum, the data analysis section of metabolomics studies should be readable and interpretable without omissions such that a data analysis workflow diagram could be extrapolated from the study and therefore the data analysis protocol could be reused by the reader. That inconsistent and patchy reporting obfuscates reproducibility is a given. However even basic understanding and reuses of protocols are hampered by the low level of detail supplied in the data analysis sections of the studies that we reviewed.


Exhaled breath volatile organic and inorganic compound composition in endstage renal disease

October 2017

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58 Reads

Nieren- und Hochdruckkrankheiten

Aims: Patients with end-stage renal disease (ESRD) are characterized by uremia and increased oxidative stress. The aim of this study was to investigate the influence of hemodialysis on breath ammonia and volatile oxidative stress parameters. Methods: Breath analysis was performed in 18 ESRD patients prior, during, and 30 minutes after a hemodialysis session. Parameters of hemodialysis efficiency and oxidative stress (lipid peroxides, total antioxidative capacity, myeloperoxidase, and malondialdehyde) were measured in blood at the beginning, after 30 minutes, and at the end of the dialysis session. 10 healthy volunteers with normal renal function served as a control group. Ion-molecule reaction mass spectrometry was used for breath-gas analysis. Results: Initial elevated concentrations of breath ammonia decreased during hemodialysis and correlated with serum urea levels (r2 = 0.74), whereas isoprene concentrations increased. Breath concentrations of malondialdehyde and pentane (MDA-P) were significantly elevated in ESRD patients (p < 0.01). Within the blood, a significant decrease of malondialdehyde was notable during hemodialysis treatment, whereas levels of lipid peroxides and myeloperoxidase increased. Conclusion: Exhaled breath of patients with ESRD on regular hemodialysis treatment is characterized by an increase in ammonia and MDA- P. The efficient decrease of breath ammonia and its close correlation to serum urea during hemodialysis suggests its possible use as a noninvasive marker to monitor dialysis efficacy.


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Citations (23)


... [1,2]. However, management of both conditions has improved: advancements 2 of 9 in stereotactic brain radiotherapy [3] and enhanced intracranial efficacy of drugs in medical oncology have led to better survival rates [4][5][6][7]. Nevertheless, higher doses administered in radiotherapy increase the risk of complications such as cerebral radionecrosis [8,9]. ...

Reference:

Are Dual-Phase F-Fluorodeoxyglucose PET-mpMRI Diagnostic Performances to Distinguish Brain Tumour Radionecrosis/Recurrence after Cranial Radiotherapy Usable in Routine?
Stereotactic Radiosurgery versus Whole-Brain Radiotherapy in Patients with 4–10 Brain Metastases: A Nonrandomized Controlled Trial
  • Citing Article
  • June 2023

Radiotherapy and Oncology

... A meta-analysis revealed the following pooled sensitivities and specificities for the differentiation of glioma recurrence and pseudo progression in MR perfusion techniques: 0.82, 0.87 for DSC, 0.83, 0.83 for DCE (Dynamic Contrast Enhanced), and 0.78, 0.86 for ASL (Arterial Spin Labeling) perfusion [27]. Sensitivity and specificity of TRAMs are approximately 0.93 and 0.78 [28]. ...

MRI-based contrast clearance analysis shows high differentiation accuracy between radiation-induced reactions and progressive disease after cranial radiotherapy

ESMO Open

... For further analysis, patients were categorized according to chemotherapy administered concomitant to re-RT. The distribution of age and gender, initial therapy, and PFS, as well as MGMT methylation status and other prognostic factors according to two prognostic scores (RRRS [16] and DKTK-ROG [17]) are detailed in Table A1 in the Appendix A. ...

Independent validation of a new reirradiation risk score (RRRS) for glioma patients predicting post-recurrence survival: A multicenter DKTK/ROG analysis
  • Citing Article
  • February 2018

Radiotherapy and Oncology

... Several reporting guidelines were developed, such as Metabolomics Standards Initiative guidelines [38], as well as ones focused on different areas of metabolomics applications and instrumentation [39][40][41][42][43]. Despite the existing efforts the quality of reporting experimental data could be improved [44]. ...

Critical review of reporting of the data analysis step in metabolomics

Metabolomics

... Benchmarking studies may be performed by independent groups interested in systematically comparing existing methods or by authors of new methods to demonstrate performance improvements or other advantages over existing competitors. With regard to studies performed by independent groups, there have been several benchmarking studies of clustering for continuous data only or categorical data only (e.g., Milligan 1980;Meilȃ and Heckerman 2001;Ferreira and Hitchcock 2009;Saraçli et al. 2013;Boulesteix and Hatz 2017;Javed et al. 2020;Hennig 2022), whereas benchmarking studies of clustering for mixed-type data are scarce (Jimeno et al. 2021;Preud'Homme et al. 2021). We can also distinguish benchmarking studies of clustering for mixed-type data that are part of original papers where new methods are proposed (e.g., Ahmad and Dey 2007;Hennig and Liao 2013;Foss et al. 2016). ...

Benchmarking for Clustering Methods Based on Real Data: A Statistical View
  • Citing Chapter
  • July 2017

... As reported by an article in 2016, IL-22 levels were significantly higher in BALF from lung cancer patients compared with control group. The researchers expanded the cohort to patients with lung metastases from other malignancies and found that IL-22 concentrations remained higher than controls [87]. These results implied that IL-22 in BALF may be a biomarker for lung cancer. ...

Interleukin-22 is elevated in lavage from patients with lung cancer and other pulmonary diseases

BMC Cancer

... Генетический риск развития заболеваний этого класса, т.е. вероятность их развития, обусловленная только генетическими причинами, как считается, формируется за счет множества генетических вариантов (генетических полиморфизмов) значительного числа генов [10]. Ведется активный поиск генетических вариантов, связанных с повышением вероятности развития болезней предрасположения, и предполагается, что в случае выявления наиболее значимых генетических маркеров будет возможен прогноз риска их развития на основе генетического профилирования [10]. ...

Improved prediction of complex diseases by common genetic markers: state of the art and further perspectives

Human Genetics

... Thus, the models containing both and considered the 200 predictors with 2 components each, resulting in 400 component variables to be included. The regularization parameter of the one-stage models was chosen by minimizing the 10-times repeated 10fold cross-validated RMSPE across a range of 50 potential values for [21,22]. For the two-stage approaches, in addition to the selection process used for the one-stage models, we conducted a grid search to identify the optimal combination of = ( 1 , 2 ) among 50 2 possible combinations (see Supplementary Material Section 1.5). ...

Complexity Selection with Cross-validation for Lasso and Sparse Partial Least Squares Using High-Dimensional Data
  • Citing Article
  • July 2013

... In biomedical research, for example, measuring the effects of biomedical markers w.r.t. model prediction is as essential as measuring their added value regarding model performance [4]. We use the term feature importance 1 to describe how important the feature was for the predictive performance of the model, regardless of the shape (e.g., linear or nonlinear relationship) or direction of the feature effect. ...

The Residual-Based Predictiveness Curve: A Visual Tool to Assess the Performance of Prediction Models

Biometrics

... The relevant variables should be frequently included in the models, while the others should be selected in few cases, corresponding to particular configurations of the pseudo-sample. However, an issue related to the use of bootstrap pseudo-samples is the tendency to select too many variables (Heymans et al., 2007;Janitza et al., 2014). According to these authors, the frequencies of irrelevant/"weak effect" variables range from 20 to 60%. ...

Pitfalls of hypothesis tests and model selection on bootstrap samples: Causes and consequences in biometrical applications
  • Citing Article
  • September 2015

Biometrical Journal