Improving shared decision making for lung cancer treatment by developing and validating an open-source web based patient decision aid for stage I-II non-small cell lung cancer

The aim of this study was to develop and evaluate a proof-of-concept open-source individualized Patient Decision Aid (iPDA) with a group of patients, physicians, and computer scientists. The iPDA was developed based on the International Patient Decision Aid Standards (IPDAS). A previously published questionnaire was adapted and used to test the user-friendliness and content of the iPDA. The questionnaire contained 40 multiple-choice questions, and answers were given on a 5-point Likert Scale (1-5) ranging from "strongly disagree" to "strongly agree." In addition to the questionnaire, semi-structured interviews were conducted with patients. We performed a descriptive analysis of the responses. The iPDA was evaluated by 28 computer scientists, 21 physicians, and 13 patients. The results demonstrate that the iPDA was found valuable by 92% (patients), 96% (computer scientists), and 86% (physicians), while the treatment information was judged useful by 92%, 96%, and 95%, respectively. Additionally, the tool was thought to be motivating for patients to actively engage in their treatment by 92%, 93%, and 91% of the above respondents groups. More multimedia components and less text were suggested by the respondents as ways to improve the tool and user interface. In conclusion, we successfully developed and tested an iPDA for patients with stage I-II Non-Small Cell Lung Cancer (NSCLC).

Effect of 0.35 T and 1.5 T magnetic fields on superficial dose in MR-guided radiotherapy of laryngeal cancer

Background

Treatment of head and neck cancer on linear accelerators with on-board magnetic resonance imaging (MR-linac) might be beneficial to reduce side effects and increase accuracy. For many head and neck cancer patients, dose coverage of the often superficially located planning target volumes (PTVs) is required. This study examines the impact of the electron return effect (ERE) on the surface dose in MR-guided radiotherapy (MRgRT) compared to conventional radiotherapy.

Materials and methods

For this bicentric dosimetric study, 14 cases of laryngeal carcinomas with PTVs reaching up to the skin surface were included. For each patient, five different plans were compared, two VMAT plans (with and without a 5 mm bolus) and three IMRT MRgRT plans (0.35 T, 1.5 T and 0 T, each without bolus). Dose distributions were also validated with film measurements.

Results

A similar coverage on the most superficial 3-5 mm of the PTV was achieved in the VMAT plans with bolus and the MRgRT plans for both 0.35 T and 1.5 T. However, coverage on this region was usually not achieved for VMAT without bolus and the 0 T plans. The film measurements on phantoms confirmed the results with the relative error never exceeding the calculated differences between the plans.

Conclusion

The present study could demonstrate that the ERE for both commercially available MR-linac variants provides sufficient coverage of the superficial tissue layers in MRgRT-plans for laryngeal carcinoma.

Predicting Adverse Radiation Effects in Brain Tumors After Stereotactic Radiotherapy With Deep Learning and Handcrafted Radiomics

Introduction

There is a cumulative risk of 20–40% of developing brain metastases (BM) in solid cancers. Stereotactic radiotherapy (SRT) enables the application of high focal doses of radiation to a volume and is often used for BM treatment. However, SRT can cause adverse radiation effects (ARE), such as radiation necrosis, which sometimes cause irreversible damage to the brain. It is therefore of clinical interest to identify patients at a high risk of developing ARE. We hypothesized that models trained with radiomics features, deep learning (DL) features, and patient characteristics or their combination can predict ARE risk in patients with BM before SRT.

Methods

Gadolinium-enhanced T1-weighted MRIs and characteristics from patients treated with SRT for BM were collected for a training and testing cohort (N = 1,404) and a validation cohort (N = 237) from a separate institute. From each lesion in the training set, radiomics features were extracted and used to train an extreme gradient boosting (XGBoost) model. A DL model was trained on the same cohort to make a separate prediction and to extract the last layer of features. Different models using XGBoost were built using only radiomics features, DL features, and patient characteristics or a combination of them. Evaluation was performed using the area under the curve (AUC) of the receiver operating characteristic curve on the external dataset. Predictions for individual lesions and per patient developing ARE were investigated.

Results

The best-performing XGBoost model on a lesion level was trained on a combination of radiomics features and DL features (AUC of 0.71 and recall of 0.80). On a patient level, a combination of radiomics features, DL features, and patient characteristics obtained the best performance (AUC of 0.72 and recall of 0.84). The DL model achieved an AUC of 0.64 and recall of 0.85 per lesion and an AUC of 0.70 and recall of 0.60 per patient.

Conclusion

Machine learning models built on radiomics features and DL features extracted from BM combined with patient characteristics show potential to predict ARE at the patient and lesion levels. These models could be used in clinical decision making, informing patients on their risk of ARE and allowing physicians to opt for different therapies.

Automated detection and segmentation of non-small cell lung cancer computed tomography images

Detection and segmentation of abnormalities on medical images is highly important for patient management including diagnosis, radiotherapy, response evaluation, as well as for quantitative image research. We present a fully automated pipeline for the detection and volumetric segmentation of non-small cell lung cancer (NSCLC) developed and validated on 1328 thoracic CT scans from 8 institutions. Along with quantitative performance detailed by image slice thickness, tumor size, image interpretation difficulty, and tumor location, we report an in-silico prospective clinical trial, where we show that the proposed method is faster and more reproducible compared to the experts. Moreover, we demonstrate that on average, radiologists & radiation oncologists preferred automatic segmentations in 56% of the cases. Additionally, we evaluate the prognostic power of the automatic contours by applying RECIST criteria and measuring the tumor volumes. Segmentations by our method stratified patients into low and high survival groups with higher significance compared to those methods based on manual contours.

Correct interpretation of computer tomography (CT) scans is important for the correct assessment of a patient’s disease but can be subjective and timely. Here, the authors develop a system that can automatically segment the non-small cell lung cancer on CT images of patients and show in an in silico trial that the method was faster and more reproducible than clinicians.

Comprehensive summary and retrospective evaluation of prognostic scores for patients with newly diagnosed brain metastases treated with upfront radiosurgery in a modern patient collective

BACKGROUND

Numerous prognostic scores (PS) for patients with brain metastases (BM) have been developed. Recently, PS based on laboratory parameters were introduced to better predict overall survival (OS). A comprehensive comparison of the wide range of scores in a modern patient collective is still missing.

MATERIALS AND METHODS

Twelve PS considering clinical parameters only at the time of BM diagnosis were calculated for 470 patients receiving upfront SRS between January 2014 and March 2020. In a subcohort of 310 patients where a full laboratory dataset was available five additional prognostic scores were compared. Restricted mean survival time (RMST), partial likelihood and c-index were calculated as metrics for performance evaluation. Univariable and multivariable analysis were used to identify prognostic factors for OS.

RESULTS

The median OS of the whole cohort was 15.8 months (95% C.I.: 13.4-20.1). All prognostic scores performed well in separating patients into different prognostic groups. RPA achieved the highest c-index, whereas GGS achieved highest partial likelihood with evaluation in the total cohort. With incorporation of the laboratory scores the recently suggested EC-GPA achieved highest c-index and highest partial likelihood. A prognostic score solely based on the assessment of performance status achieved considerable high performance as either 3- or 4-tiered score. Multivariable analysis revealed performance status, systemic disease status and laboratory parameters to be significantly associated with OS among variates included in prognostic scores.

CONCLUSION

Although recent PS incorporating laboratory parameters show convincing performance in predicting overall survival, older scores relying on clinical parameters only are still valid and appealing as they are easier to calculate, and as overall performance is almost equal. Moreover, a score just based on performance status is not significantly inferior and should at least be assessed for informed decision making.

Dental extraction, intensity-modulated radiotherapy of head and neck cancer, and osteoradionecrosis : A systematic review and meta-analysis

Objective

To seek evidence for osteoradionecrosis (ORN) after dental extractions before or after intensity-modulated radiotherapy (IMRT) for head and neck cancer (HNC).

Methods

Medline/PubMed, Embase, and Cochrane Library were searched from 2000 until 2020. Articles on HNC patients treated with IMRT and dental extractions were analyzed by two independent reviewers. The risk ratios (RR) and odds ratios (OR) for ORN related to extractions were calculated using Fisher’s exact test. A one-sample proportion test was used to assess the proportion of pre- versus post-IMRT extractions. Forest plots were used for the pooled RR and OR using a random-effects model.

Results

Seven of 630 publications with 875 patients were eligible. A total of 437 (49.9%) patients were treated with extractions before and 92 (10.5%) after IMRT. 28 (3.2%) suffered from ORN after IMRT. ORN was associated with extractions in 15 (53.6%) patients, eight related to extractions prior to and seven cases related to extractions after IMRT. The risk and odds for ORN favored pre-IMRT extractions (RR = 0.18, 95% CI: 0.04–0.74, p = 0.031, I² = 0%, OR = 0.16, 95% CI: 0.03–0.99, p = 0.049, I² = 0%). However, the prediction interval of the expected range of 95% of true effects included 1 for RR and OR.

Conclusion

Tooth extraction before IMRT is more common than after IMRT, but dental extractions before compared to extractions after IMRT have not been proven to reduce the incidence of ORN. Extractions of teeth before IMRT have to be balanced with any potential delay in initiating cancer therapy.

Supplementary Information

The online version of this article (10.1007/s00066-021-01896-w) contains supplementary material, which is available to authorized users.

A 2.5D convolutional neural network for HPV prediction in advanced oropharyngeal cancer

BACKGROUND

Infection with human papilloma virus (HPV) is one of the most relevant prognostic factors in advanced oropharyngeal cancer (OPC) treatment. In this study we aimed to assess the diagnostic accuracy of a deep learning-based method for HPV status prediction in computed tomography (CT) images of advanced OPC.

METHOD

An internal dataset and three public collections were employed (internal: n = 151, HNC1: n = 451; HNC2: n = 80; HNC3: n = 110). Internal and HNC1 datasets were used for training, whereas HNC2 and HNC3 collections were used as external test cohorts. All CT scans were resampled to a 2 mm³ resolution and a sub-volume of 72x72x72 pixels was cropped on each scan, centered around the tumor. Then, a 2.5D input of size 72x72x3 pixels was assembled by selecting the 2D slice containing the largest tumor area along the axial, sagittal and coronal planes, respectively. The convolutional neural network employed consisted of the first 5 modules of the Xception model and a small classification network. Ten-fold cross-validation was applied to evaluate training performance. At test time, soft majority voting was used to predict HPV status.

RESULTS

A final training mean [range] area under the curve (AUC) of 0.84 [0.76-0.89], accuracy of 0.76 [0.64-0.83] and F1-score of 0.74 [0.62-0.83] were achieved. AUC/accuracy/F1-score values of 0.83/0.75/0.69 and 0.88/0.79/0.68 were achieved on the HNC2 and HNC3 test sets, respectively.

CONCLUSION

Deep learning was successfully applied and validated in two external cohorts to predict HPV status in CT images of advanced OPC, proving its potential as a support tool in cancer precision medicine.

Single-isocenter versus multiple-isocenters for multiple lung metastases: Evaluation of lung dose

BACKGROUND AND PURPOSE

A potential challenge in single-isocenter multi-lesion lung stereotactic body radiotherapy (SBRT) is that patient positioning is not based on each lesion individually, but on the average position of all lesions. This may lead to larger margins compared to treating with one isocenter per lesion, but increases workflow efficiency. The aim of this study was to investigate whether a single-isocenter technique leads to increased normal lung dose compared to a conventional multiple-isocenters technique.

MATERIALS AND METHODS

A cohort of 15 NSCLC patients with two or three lesions previously treated with SBRT was subjected to treatment planning with a multiple-isocenter technique and a single-isocenter technique. For the latter, two margin approaches were evaluated: (1) identical margins for each internal target volume (ITV), assuming an average registration for all lesions in cone-beam CT (CBCT) positioning verification and (2) a smaller margin for the largest lesion, assuming an optimal registration for that lesion. For all 45 treatment plans, mean lung dose (MLD) and lungs-V_20Gy were evaluated. The study was performed following RATING guidelines.

RESULTS

The MLD was 4.9 ± 1.9 Gy (mean ± SD) for multiple-isocenters and 5.4 ± 2.1 Gy and 5.3 ± 2.2 Gy for single-isocenter approach 1 and 2, respectively. V_20Gy was 5.5 ± 3.7%, 5.5 ± 3.2% and 5.4 ± 3.3%. A median [range] increase in MLD of 11.6% [-14.9 - 26.8] was observed when comparing single-isocenter treatment plans to those with multiple isocenters. V_20Gy increased by 0.2 [-3.4 - 1.3] percentage points.

CONCLUSION

A single-isocenter SBRT technique for lung patients with multiple targets results in clinically acceptable increases in normal lung dose.

Quantification of the spatial distribution of primary tumors in the lung to develop new prognostic biomarkers for locally advanced NSCLC

The anatomical location and extent of primary lung tumors have shown prognostic value for overall survival (OS). However, its manual assessment is prone to interobserver variability. This study aims to use data driven identification of image characteristics for OS in locally advanced non-small cell lung cancer (NSCLC) patients. Five stage IIIA/IIIB NSCLC patient cohorts were retrospectively collected. Patients were treated either with radiochemotherapy (RCT): RCT1* (n = 107), RCT2 (n = 95), RCT3 (n = 37) or with surgery combined with radiotherapy or chemotherapy: S1* (n = 135), S2 (n = 55). Based on a deformable image registration (MIM Vista, 6.9.2.), an in-house developed software transferred each primary tumor to the CT scan of a reference patient while maintaining the original tumor shape. A frequency-weighted cumulative status map was created for both exploratory cohorts (indicated with an asterisk), where the spatial extent of the tumor was uni-labeled with 2 years OS. For the exploratory cohorts, a permutation test with random assignment of patient status was performed to identify regions with statistically significant worse OS, referred to as decreased survival areas (DSA). The minimal Euclidean distance between primary tumor to DSA was extracted from the independent cohorts (negative distance in case of overlap). To account for the tumor volume, the distance was scaled with the radius of the volume-equivalent sphere. For the S1 cohort, DSA were located at the right main bronchus whereas for the RCT1 cohort they further extended in cranio-caudal direction. In the independent cohorts, the model based on distance to DSA achieved performance: AUC_RCT2 [95% CI] = 0.67 [0.55–0.78] and AUC_RCT3 = 0.59 [0.39–0.79] for RCT patients, but showed bad performance for surgery cohort (AUC_S2 = 0.52 [0.30–0.74]). Shorter distance to DSA was associated with worse outcome (p = 0.0074). In conclusion, this explanatory analysis quantifies the value of primary tumor location for OS prediction based on cumulative status maps. Shorter distance of primary tumor to a high-risk region was associated with worse prognosis in the RCT cohort.

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

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.

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

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 (p-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 (p < 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.

Systematic Review on the Association of Radiomics with Tumor Biological Endpoints

Radiomics supposes an alternative non-invasive tumor characterization tool, which has experienced increased interest with the advent of more powerful computers and more sophisticated machine learning algorithms. Nonetheless, the incorporation of radiomics in cancer clinical-decision support systems still necessitates a thorough analysis of its relationship with tumor biology. Herein, we present a systematic review focusing on the clinical evidence of radiomics as a surrogate method for tumor molecular profile characterization. An extensive literature review was conducted in PubMed, including papers on radiomics and a selected set of clinically relevant and commonly used tumor molecular markers. We summarized our findings based on different cancer entities, additionally evaluating the effect of different modalities for the prediction of biomarkers at each tumor site. Results suggest the existence of an association between the studied biomarkers and radiomics from different modalities and different tumor sites, even though a larger number of multi-center studies are required to further validate the reported outcomes.

Head and neck radiotherapy on the MR linac: a multicenter planning challenge amongst MRIdian platform users

Purpose

Purpose of this study is to evaluate plan quality on the MRIdian (Viewray Inc., Oakwood Village, OH, USA) system for head and neck cancer (HNC) through comparison of planning approaches of several centers.

Methods

A total of 14 planners using the MRIdian planning system participated in this treatment challenge, centrally organized by ViewRay, for one contoured case of oropharyngeal carcinoma with standard constraints for organs at risk (OAR). Homogeneity, conformity, sparing of OARs, and other parameters were evaluated according to The International Commission on Radiation Units and Measurements (ICRU) recommendations anonymously, and then compared between centers. Differences amongst centers were assessed by means of Wilcoxon test. Each plan had to fulfil hard constraints based on dose–volume histogram (DVH) parameters and delivery time. A plan quality metric (PQM) was evaluated. The PQM was defined as the sum of 16 submetrics characterizing different DVH goals.

Results

For most dose parameters the median score of all centers was higher than the threshold that results in an ideal score. Six participants achieved the maximum number of points for the OAR dose parameters, and none had an unacceptable performance on any of the metrics. Each planner was able to achieve all the requirements except for one which exceeded delivery time. The number of segments correlated to improved PQM and inversely correlated to brainstem D_0.1cc and to Planning Target Volume1 (PTV) D_0.1cc. Total planning experience inversely correlated to spinal canal dose.

Conclusion

Magnetic Resonance Image (MRI) linac-based planning for HNC is already feasible with good quality. Generally, an increased number of segments and increasing planning experience are able to provide better results regarding planning quality without significantly prolonging overall treatment time.

Supplementary Information

The online version of this article (10.1007/s00066-021-01771-8) contains supplementary material, which is available to authorized users.

Cochlea sparing optimized radiotherapy for nasopharyngeal carcinoma

BACKGROUND

Definitive chemoradiotherapy (CRT) is standard of care for nasopharyngeal carcinoma. Due to the tumor localization and concomitant platinum-based chemotherapy, hearing impairment is a frequent complication, without defined dose-threshold. In this study, we aimed to achieve the maximum possible cochleae sparing.

MATERIALS AND METHODS

Treatment plans of 20 patients, treated with CRT (6 IMRT and 14 VMAT) based on the QUANTEC organs-at-risk constraints were investigated. The cochleae were re-delineated independently by two radiation oncologists, whereas target volumes and other organs at risk (OARs) were not changed. The initial plans, aiming to a mean cochlea dose < 45 Gy, were re-optimized with VMAT, using 2-2.5 arcs without compromising the dose coverage of the target volume. Mean cochlea dose, PTV coverage, Homogeneity Index, Conformity Index and dose to other OAR were compared to the reference plans. Wilcoxon signed-rank test was used to evaluate differences, a p value < 0.05 was considered significant.

RESULTS

The re-optimized plans achieved a statistically significant lower dose for both cochleae (median dose for left and right 14.97 Gy and 18.47 Gy vs. 24.09 Gy and 26.05 Gy respectively, p < 0.001) compared to the reference plans, without compromising other plan quality parameters. The median NTCP for tinnitus of the most exposed sites was 11.3% (range 3.52-91.1%) for the original plans, compared to 4.60% (range 1.46-90.1%) for the re-optimized plans (p < 0.001). For hearing loss, the median NTCP of the most exposed sites could be improved from 0.03% (range 0-99.0%) to 0.00% (range 0-98.5%, p < 0.001).

CONCLUSIONS

A significantly improved cochlea sparing beyond current QUANTEC constraints is feasible without compromising the PTV dose coverage in nasopharyngeal carcinoma patients treated with VMAT. As there appears to be no threshold for hearing toxicity after CRT, this should be considered for future treatment planning.

Distance to isocenter is not associated with an increased risk for local failure in LINAC-based single-isocenter SRS or SRT for multiple brain metastases

PURPOSE

To evaluate the impact of the distance between treatment isocenter and brain metastases on local failure in patients treated with a frameless linear-accelerator-based single-isocenter volumetric modulated arc (VMAT) SRS/SRT for multiple brain metastases.

METHODS AND MATERIALS

Patients treated with SRT for brain metastases (BM) between April 2014 and May 2019 were included in this retrospective study. BM treated with a single-isocenter multiple-target (SIMT) SRT were evaluated for local recurrence-free intervals in dependency to their distance to the treatment isocenter. A Cox-regression model was used to investigate different predictor variables for local failure. Results were compared to patients treated with a single-isocenter-single-target (SIST) approach.

RESULTS

In total 315 patients with a cumulative number of 1087 BM were analyzed in this study of which 140 patients and 708 BM were treated with SIMT SRS/SRT. Median follow-up after treatment was 13.9 months for SIMT approach and 11.9 months for SIST approach. One-year freedom from local recurrence was 87% and 94% in the SIST and SIMT group, respectively. Median distance to isocenter (DTI) was 4.7 cm (range 0.2-10.5) in the SIMT group. Local recurrence-free interval was not associated with the distance to the isocenter in univariable or multivariable Cox-regression analysis. Multivariable analysis revealed only volume as an independent significant predictor for local failure (p-value <0.05).

CONCLUSION

SRS/SRT using single-isocenter VMAT for multiple targets achieved high local metastases control rates irrespective of distance to the isocenter, supporting efficacy of single-isocenter stereotactic radiation therapy for multiple brain metastases.

MR-Guided Radiotherapy for Head and Neck Cancer: Current Developments, Perspectives, and Challenges

Based on the development of new hybrid machines consisting of an MRI and a linear accelerator, magnetic resonance image guided radiotherapy (MRgRT) has revolutionized the field of adaptive treatment in recent years. Although an increasing number of studies have been published, investigating technical and clinical aspects of this technique for various indications, utilizations of MRgRT for adaptive treatment of head and neck cancer (HNC) remains in its infancy. Yet, the possible benefits of this novel technology for HNC patients, allowing for better soft-tissue delineation, intra- and interfractional treatment monitoring and more frequent plan adaptations appear more than obvious. At the same time, new technical, clinical, and logistic challenges emerge. The purpose of this article is to summarize and discuss the rationale, recent developments, and future perspectives of this promising radiotherapy modality for treating HNC.

Comparison of beam segment versus full plan re-optimization in daily magnetic resonance imaging-guided online-adaptive radiotherapy

The optimal approach for magnetic resonance imaging-guided online adaptive radiotherapy is currently unknown and needs to consider patient on-couch time constraints. The aim of this study was to compare two different plan optimization approaches. The comparison was performed in 238 clinically applied online-adapted treatment plans from 55 patients, in which the approach of re-optimization was selected based on the physician’s choice. For 33 patients where both optimization approaches were used at least once, the median treatment planning dose metrics of both target and organ at risk differed less than 1%. Therefore, we concluded that beam segment weight optimization was chosen adequately for most patients without compromising plan quality.

Treatment plan quality during online adaptive re-planning

BACKGROUND

Online adaptive radiotherapy is intended to prevent plan degradation caused by inter-fractional tumor volume and shape changes, but time limitations make online re-planning challenging. The aim of this study was to compare the quality of online-adapted plans to their respective reference treatment plans.

METHODS

Fifty-two patients treated on a ViewRay MRIdian Linac were included in this retrospective study. In total 238 online-adapted plans were analyzed, which were optimized with either changing of the segment weights (n = 85) or full re-optimization (n = 153). Five different treatment sites were evaluated: prostate, abdomen, liver, lung and pelvis. Dosimetric parameters of gross tumor volume (GTV), planning target volume (PTV), 2 cm ring around the PTV and organs at risk (OARs) were considered. The Wilcoxon signed-rank test was used to assess differences between online-adapted and reference treatment plans, p < 0.05 was considered significant.

RESULTS

The average duration of the online adaptation, consisting of contour editing, plan optimization and quality assurance (QA), was 24 ± 6 min. The GTV was slightly larger (average ± SD: 1.9% ± 9.0%) in the adapted plans than in the reference plans (p < 0.001). GTV-D95% exhibited no significant changes when considering all plans, but GTV-D2% increased by 0.40% ± 1.5% on average (p < 0.001). There was a very small yet significant decrease in GTV-coverage for the abdomen plans. The ring Dmean increased on average by 1.0% ± 3.6% considering all plans (p < 0.001). There was a significant reduction of the dose to the rectum of 4.7% ± 16% on average (p < 0.001) for prostate plans.

CONCLUSIONS

Dosimetric quality of online-adapted plans was comparable to reference treatment plans and OAR dose was either comparable or decreased, depending on treatment site. However, dose spillage was slightly increased.

Radiomics in medical imaging-"how-to" guide and critical reflection

Radiomics is a quantitative approach to medical imaging, which aims at enhancing the existing data available to clinicians by means of advanced mathematical analysis. Through mathematical extraction of the spatial distribution of signal intensities and pixel interrelationships, radiomics quantifies textural information by using analysis methods from the field of artificial intelligence. Various studies from different fields in imaging have been published so far, highlighting the potential of radiomics to enhance clinical decision-making. However, the field faces several important challenges, which are mainly caused by the various technical factors influencing the extracted radiomic features.The aim of the present review is twofold: first, we present the typical workflow of a radiomics analysis and deliver a practical "how-to" guide for a typical radiomics analysis. Second, we discuss the current limitations of radiomics, suggest potential improvements, and summarize relevant literature on the subject.

Radiomics: from qualitative to quantitative imaging

Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes. Radiomics, in its two forms "handcrafted and deep," is an emerging field that translates medical images into quantitative data to yield biological information and enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, and monitoring. Handcrafted radiomics is a multistage process in which features based on shape, pixel intensities, and texture are extracted from radiographs. Within this review, we describe the steps: starting with quantitative imaging data, how it can be extracted, how to correlate it with clinical and biological outcomes, resulting in models that can be used to make predictions, such as survival, or for detection and classification used in diagnostics. The application of deep learning, the second arm of radiomics, and its place in the radiomics workflow is discussed, along with its advantages and disadvantages. To better illustrate the technologies being used, we provide real-world clinical applications of radiomics in oncology, showcasing research on the applications of radiomics, as well as covering its limitations and its future direction.