Wilhelm Conrad Röntgen: The scientist and his discovery.

Röntgen's discovery of a new type of radiation is the epochal event in a series of highlights of physics emerging within only a few decades of the late 19th century. As these discoveries are directly or indirectly rooting in the study of the phenomenon of electric discharge in gases a brief look at the physics scenario in the immediate pre-X-ray era is presented in the first section. Rather than just the fortune with the bold it is Röntgen's character as a diligent, self-critical and ingenious scientist which made his discovery possible. This will be illustrated in the sections on Röntgen's personal life and some specific details of his experiments leading to the discovery of X-rays. Finally, a short overview is given on the potential and the large variety of applications of X-rays.

MRI- and CT-determined changes of dysphagia / aspiration-related structures (DARS) during and after radiotherapy.

PURPOSE: The concept of dysphagia/aspiration-related structures (DARS) was developed against the background of severe late side effects of radiotherapy (RT) for head and neck cancer (HNC). DARS can be delineated on CT scans, but with a better morphological discrimination on magnetic resonance imaging (MRI). Swallowing function was analyzed by use of patient charts and prospective investigations and questionnaires. METHOD: Seventeen HNC patients treated with intensity-modulated radiotherapy (IMRT) ± chemotherapy between 5/2012 - 8/2015 were included. Planning CT (computed tomography) scans and MRIs (magnetic resonance imaging) prior, during 40 Gray (Gy) radiotherapy and posttreatment were available and co-registered to delineate DARS. The RT dose of each DARS was calculated. Five patients were investigated posttreatment for swallowing function and assessed by means of various questionnaires for quality of life (QoL), swallowing, and voice function. RESULTS: By retrospective comparison of DARS volume, a significant change in four of eight DARS was detected over time. Three increased and one diminished. The risk of posttreatment dysphagia rose by every 1Gy above the mean dose (D mean) of RT to DARS. 7.5 was the risk factor for dysphagia in the first 6 months, reducing to 4.7 for months 6-12 posttreatment. For all five patients of the prospective part of swallowing investigations, a function disturbance was detected. These results were in contrast to the self-assessment of patients by questionnaires. There was neither a dose dependency of D mean DARS volume changes over time nor of dysphonia and no correlation between volume changes, dysphagia or dysphonia. CONCLUSION: Delineation of DARS on MRI co-registered to planning CT gave the opportunity to differentiate morphology better than by CT alone. Due to the small number of patients with complete MRI scans over time, we failed to detect a dose dependency of DARS and swallowing and voice disorder posttreatment.

Tumor grading of soft tissue sarcomas using MRI-based radiomics.

BACKGROUND: Treatment decisions for multimodal therapy in soft tissue sarcoma (STS) patients greatly depend on the differentiation between low-grade and high-grade tumors. We developed MRI-based radiomics grading models for the differentiation between low-grade (G1) and high-grade (G2/G3) STS. METHODS: The study was registered at ClinicalTrials.gov (number NCT03798795). Contrast-enhanced T1-weighted fat saturated (T1FSGd), fat-saturated T2-weighted (T2FS) MRI sequences, and tumor grading following the French Federation of Cancer Centers Sarcoma Group obtained from pre-therapeutic biopsies were gathered from two independent retrospective patient cohorts. Volumes of interest were manually segmented. After preprocessing, 1394 radiomics features were extracted from each sequence. Features unstable in 21 independent multiple-segmentations were excluded. Least absolute shrinkage and selection operator models were developed using nested cross-validation on a training patient cohort (122 patients). The influence of ComBatHarmonization was assessed for correction of batch effects. FINDINGS: Three radiomic models based on T2FS, T1FSGd and a combined model achieved predictive performances with an area under the receiver operator characteristic curve (AUC) of 0.78, 0.69, and 0.76 on the independent validation set (103 patients), respectively. The T2FS-based model showed the best reproducibility. The radiomics model involving T1FSGd-based features achieved significant patient stratification. Combining the T2FS radiomic model into a nomogram with clinical staging improved prognostic performance and the clinical net benefit above clinical staging alone. INTERPRETATION: MRI-based radiomics tumor grading models effectively classify low-grade and high-grade soft tissue sarcomas. FUND: The authors received support by the medical faculty of the Technical University of Munich and the German Cancer Consortium.

CT-based radiomic features predict tumor grading and have prognostic value in patients with soft tissue sarcomas treated with neoadjuvant radiation therapy.

PURPOSE: In soft tissue sarcoma (STS) patients systemic progression and survival remain comparably low despite low local recurrence rates. In this work, we investigated whether quantitative imaging features ("radiomics") of radiotherapy planning CT-scans carry a prognostic value for pre-therapeutic risk assessment. METHODS: CT-scans, tumor grade, and clinical information were collected from three independent retrospective cohorts of 83 (TUM), 87 (UW) and 51 (McGill) STS patients, respectively. After manual segmentation and preprocessing, 1358 radiomic features were extracted. Feature reduction and machine learning modeling for the prediction of grading, overall survival (OS), distant (DPFS) and local (LPFS) progression free survival were performed followed by external validation. RESULTS: Radiomic models were able to differentiate grade 3 from non-grade 3 STS (area under the receiver operator characteristic curve (AUC): 0.64). The Radiomic models were able to predict OS (C-index: 0.73), DPFS (C-index: 0.68) and LPFS (C-index: 0.77) in the validation cohort. A combined clinical-radiomics model showed the best prediction for OS (C-index: 0.76). The radiomic scores were significantly associated in univariate and multivariate cox regression and allowed for significant risk stratification for all three endpoints. CONCLUSION: This is the first report demonstrating a prognostic potential and tumor grading differentiation by CT-based radiomics.

Combining multimodal imaging and treatment features improves machine learning-based prognostic assessment in patients with glioblastoma multiforme.

BACKGROUND: For Glioblastoma (GBM), various prognostic nomograms have been proposed. This study aims to evaluate machine learning models to predict patients' overall survival (OS) and progression-free survival (PFS) on the basis of clinical, pathological, semantic MRI-based, and FET-PET/CT-derived information. Finally, the value of adding treatment features was evaluated. METHODS: One hundred and eighty-nine patients were retrospectively analyzed. We assessed clinical, pathological, and treatment information. The VASARI set of semantic imaging features was determined on MRIs. Metabolic information was retained from preoperative FET-PET/CT images. We generated multiple random survival forest prediction models on a patient training set and performed internal validation. Single feature class models were created including "clinical," "pathological," "MRI-based," and "FET-PET/CT-based" models, as well as combinations. Treatment features were combined with all other features. RESULTS: Of all single feature class models, the MRI-based model had the highest prediction performance on the validation set for OS (C-index: 0.61 [95% confidence interval: 0.51-0.72]) and PFS (C-index: 0.61 [0.50-0.72]). The combination of all features did increase performance above all single feature class models up to C-indices of 0.70 (0.59-0.84) and 0.68 (0.57-0.78) for OS and PFS, respectively. Adding treatment information further increased prognostic performance up to C-indices of 0.73 (0.62-0.84) and 0.71 (0.60-0.81) on the validation set for OS and PFS, respectively, allowing significant stratification of patient groups for OS. CONCLUSIONS: MRI-based features were the most relevant feature class for prognostic assessment. Combining clinical, pathological, and imaging information increased predictive power for OS and PFS. A further increase was achieved by adding treatment features.

Radiomics in radiooncology - Challenging the medical physicist.

PURPOSE: Noticing the fast growing translation of artificial intelligence (AI) technologies to medical image analysis this paper emphasizes the future role of the medical physicist in this evolving field. Specific challenges are addressed when implementing big data concepts with high-throughput image data processing like radiomics and machine learning in a radiooncology environment to support clinical decisions. METHODS: Based on the experience of our interdisciplinary radiomics working group, techniques for processing minable data, extracting radiomics features and associating this information with clinical, physical and biological data for the development of prediction models are described. A special emphasis was placed on the potential clinical significance of such an approach. RESULTS: Clinical studies demonstrate the role of radiomics analysis as an additional independent source of information with the potential to influence the radiooncology practice, i.e. to predict patient prognosis, treatment response and underlying genetic changes. Extending the radiomics approach to integrate imaging, clinical, genetic and dosimetric data ('panomics') challenges the medical physicist as member of the radiooncology team. CONCLUSIONS: The new field of big data processing in radiooncology offers opportunities to support clinical decisions, to improve predicting treatment outcome and to stimulate fundamental research on radiation response both of tumor and normal tissue. The integration of physical data (e.g. treatment planning, dosimetric, image guidance data) demands an involvement of the medical physicist in the radiomics approach of radiooncology. To cope with this challenge national and international organizations for medical physics should organize more training opportunities in artificial intelligence technologies in radiooncology.

Treatment-related features improve machine learning prediction of prognosis in soft tissue sarcoma patients.

BACKGROUND AND PURPOSE: Current prognostic models for soft tissue sarcoma (STS) patients are solely based on staging information. Treatment-related data have not been included to date. Including such information, however, could help to improve these models. MATERIALS AND METHODS: A single-center retrospective cohort of 136 STS patients treated with radiotherapy (RT) was analyzed for patients' characteristics, staging information, and treatment-related data. Therapeutic imaging studies and pathology reports of neoadjuvantly treated patients were analyzed for signs of response. Random forest machine learning-based models were used to predict patients' death and disease progression at 2 years. Pre-treatment and treatment models were compared. RESULTS: The prognostic models achieved high performances. Using treatment features improved the overall performance for all three classification types: prediction of death, and of local and systemic progression (area under the receiver operatoring characteristic curve (AUC) of 0.87, 0.88, and 0.84, respectively). Overall, RT-related features, such as the planning target volume and total dose, had preeminent importance for prognostic performance. Therapy response features were selected for prediction of disease progression. CONCLUSIONS: A machine learning-based prognostic model combining known prognostic factors with treatment- and response-related information showed high accuracy for individualized risk assessment. This model could be used for adjustments of follow-up procedures.

Semantic imaging features predict disease progression and survival in glioblastoma multiforme patients.

BACKGROUND: For glioblastoma (GBM), multiple prognostic factors have been identified. Semantic imaging features were shown to be predictive for survival prediction. No similar data have been generated for the prediction of progression. The aim of this study was to assess the predictive value of the semantic visually accessable REMBRANDT [repository for molecular brain neoplasia data] images (VASARI) imaging feature set for progression and survival, and the creation of joint prognostic models in combination with clinical and pathological information. METHODS: 189 patients were retrospectively analyzed. Age, Karnofsky performance status, gender, and MGMT promoter methylation and IDH mutation status were assessed. VASARI features were determined on pre- and postoperative MRIs. Predictive potential was assessed with univariate analyses and Kaplan-Meier survival curves. Following variable selection and resampling, multivariate Cox regression models were created. Predictive performance was tested on patient test sets and compared between groups. The frequency of selection for single variables and variable pairs was determined. RESULTS: For progression free survival (PFS) and overall survival (OS), univariate significant associations were shown for 9 and 10 VASARI features, respectively. Multivariate models yielded concordance indices significantly different from random for the clinical, imaging, combined, and combined + MGMT models of 0.657, 0.636, 0.694, and 0.716 for OS, and 0.602, 0.604, 0.633, and 0.643 for PFS. "Multilocality," "deep white-matter invasion," "satellites," and "ependymal invasion" were over proportionally selected for multivariate model generation, underlining their importance. CONCLUSIONS: We demonstrated a predictive value of several qualitative imaging features for progression and survival. The performance of prognostic models was increased by combining clinical, pathological, and imaging features.

Nuclear and radiological emergencies: Building capacity in medical physics to support response.

Medical physicists represent a valuable asset at the disposal of a structured and planned response to nuclear or radiological emergencies (NREs), especially in the hospital environment. The recognition of this fact led the International Atomic Energy Agency (IAEA) and the International Organization for Medical Physics (IOMP) to start a fruitful collaboration aiming to improve education and training of medical physicists so that they may support response efforts in case of NREs. Existing shortcomings in specific technical areas were identified through international consultations supported by the IAEA and led to the development of a project aiming at preparing a specific and standardized training package for medical physicists in support to NREs. The Project was funded through extra-budgetary contribution from Japan within the IAEA Nuclear Safety Action Plan. This paper presents the work accomplished through that project and describes the current steps and future direction for enabling medical physicists to better support response to NREs.

"Radio-oncomics" : The potential of radiomics in radiation oncology.

INTRODUCTION: Radiomics, a recently introduced concept, describes quantitative computerized algorithm-based feature extraction from imaging data including computer tomography (CT), magnetic resonance imaging (MRT), or positron-emission tomography (PET) images. For radiation oncology it offers the potential to significantly influence clinical decision-making and thus therapy planning and follow-up workflow. METHODS: After image acquisition, image preprocessing, and defining regions of interest by structure segmentation, algorithms are applied to calculate shape, intensity, texture, and multiscale filter features. By combining multiple features and correlating them with clinical outcome, prognostic models can be created. RESULTS: Retrospective studies have proposed radiomics classifiers predicting, e. g., overall survival, radiation treatment response, distant metastases, or radiation-related toxicity. Besides, radiomics features can be correlated with genomic information ("radiogenomics") and could be used for tumor characterization. DISCUSSION: Distinct patterns based on data-based as well as genomics-based features will influence radiation oncology in the future. Individualized treatments in terms of dose level adaption and target volume definition, as well as other outcome-related parameters will depend on radiomics and radiogenomics. By integration of various datasets, the prognostic power can be increased making radiomics a valuable part of future precision medicine approaches. CONCLUSION: This perspective demonstrates the evidence for the radiomics concept in radiation oncology. The necessity of further studies to integrate radiomics classifiers into clinical decision-making and the radiation therapy workflow is emphasized.

Matching the reaction-diffusion simulation to dynamic [18F]FMISO PET measurements in tumors: extension to a flow-limited oxygen-dependent model.

Positron-emission tomography (PET) with hypoxia specific tracers provides a noninvasive method to assess the tumor oxygenation status. Reaction-diffusion models have advantages in revealing the quantitative relation between in vivo imaging and the tumor microenvironment. However, there is no quantitative comparison of the simulation results with the real PET measurements yet. The lack of experimental support hampers further applications of computational simulation models. This study aims to compare the simulation results with a preclinical [18F]FMISO PET study and to optimize the reaction-diffusion model accordingly. Nude mice with xenografted human squamous cell carcinomas (CAL33) were investigated with a 2 h dynamic [18F]FMISO PET followed by immunofluorescence staining using the hypoxia marker pimonidazole and the endothelium marker CD 31. A large data pool of tumor time-activity curves (TAC) was simulated for each mouse by feeding the arterial input function (AIF) extracted from experiments into the model with different configurations of the tumor microenvironment. A measured TAC was considered to match a simulated TAC when the difference metric was below a certain, noise-dependent threshold. As an extension to the well-established Kelly model, a flow-limited oxygen-dependent (FLOD) model was developed to improve the matching between measurements and simulations. The matching rate between the simulated TACs of the Kelly model and the mouse PET data ranged from 0 to 28.1% (on average 9.8%). By modifying the Kelly model to an FLOD model, the matching rate between the simulation and the PET measurements could be improved to 41.2-84.8% (on average 64.4%). Using a simulation data pool and a matching strategy, we were able to compare the simulated temporal course of dynamic PET with in vivo measurements. By modifying the Kelly model to a FLOD model, the computational simulation was able to approach the dynamic [18F]FMISO measurements in the investigated tumors.

Individualized radiotherapy by combining high-end irradiation and magnetic resonance imaging.

Image-guided radiotherapy (IGRT) has been integrated into daily clinical routine and can today be considered the standard especially with high-dose radiotherapy. Currently imaging is based on MV- or kV-CT, which has clear limitations especially in soft-tissue contrast. Thus, combination of magnetic resonance (MR) imaging and high-end radiotherapy opens a new horizon. The intricate technical properties of MR imagers pose a challenge to technology when combined with radiation technology. Several solutions that are almost ready for routine clinical application have been developed. The clinical questions include dose-escalation strategies, monitoring of changes during treatment as well as imaging without additional radiation exposure during treatment.

Marie Curie's contribution to Medical Physics.

On occasion of its 50th anniversary, the International Organization for Medical Physics (IOMP) from now on is going to celebrate annually an International Day of Medical Physics for which the 7th November, the birthday of Marie Sklodowska Curie, a most exceptional character in science at all and a pioneer of medical physics, has been chosen. This article briefly outlines her outstanding personality, sketches her fundamental discovery of radioactivity and emphasizes the impact of her various achievements on the development of medical physics at large.

Sparse dose painting based on a dual-pass kinetic-oxygen mapping of dynamic PET images.

Development of molecular imaging such as positron-emission tomography (PET) offers an opportunity to optimize radiotherapy treatment planning by conforming the dose distribution to physiological details within tumors, so called dose painting. Quantification of the acquired images and an efficient and practical dose prescription remain two key questions in this field. This paper proposes a novel framework to optimize the dose prescription based on dual-pass modeling of dynamic [18F]FMISO PET images. An optimization algorithm for sparse dose painting (SDP) is developed by minimizing a linear combination of two terms corresponding to the efficiency and total variation of the dose distribution with the constraint of a constant mean dose. Dose efficiency is defined using the linear-quadratic model. The radiosensitivity given by the oxygen tension is estimated using a dual-pass kinetic-oxygen mapping strategy. This is achieved by integrating a realistic [18F]FMISO PET imaging simulation model, which can simulate the distribution of oxygen and tracer under the same tumor microenvironment setting. The algorithm was compared with a typical dose painting by number (DPBN) method in one data set of a patient with head and neck cancer.