Robustness of radiomic features in magnetic resonance imaging for patients with glioblastoma: Multi-center study

Background and purpose Radiomics offers great potential in improving diagnosis and treatment for patients with glioblastoma multiforme. However, in order to implement radiomics in clinical routine, the features used for prognostic modelling need to be stable. This comprises significant challenge in multi-center studies. The aim of this study was to evaluate the impact of different image normalization methods on MRI features robustness in multi-center study. Methods Radiomics stability was checked on magnetic resonance images of eleven patients. The images were acquired in two different hospitals using contrast-enhanced T1 sequences. The images were normalized using one of five investigated approaches including grey-level discretization, histogram matching and z-score. Then, radiomic features were extracted and features stability was evaluated using intra-class correlation coefficients. In the second part of the study, improvement in the prognostic performance of features was tested on 60 patients derived from publicly available dataset. Results Depending on the normalization scheme, the percentage of stable features varied from 3.4% to 8%. The histogram matching based on the tumor region showed the highest amount of the stable features (113/1404); while normalization using fixed bin size resulted in 48 stable features. The histogram matching also led to better prognostic value (median c-index increase of 0.065) comparing to non-normalized images. Conclusions MRI normalization plays an important role in radiomics. Appropriate normalization helps to select robust features, which can be used for prognostic modelling in multicenter studies. In our study, histogram matching based on tumor region improved both stability of radiomic features and their prognostic value

Corrigendum to "Robustness of radiomic features in magnetic resonance imaging for patients with glioblastoma: Multi-center study" [Phys. Imaging Radiat. Oncol. 22 (2022) 131-136]

Background and purpose Radiomics offers great potential in improving diagnosis and treatment for patients with glioblastoma multiforme. However, in order to implement radiomics in clinical routine, the features used for prognostic modelling need to be stable. This comprises significant challenge in multi-center studies. The aim of this study was to evaluate the impact of different image normalization methods on MRI features robustness in multi-center study. Methods Radiomics stability was checked on magnetic resonance images of eleven patients. The images were acquired in two different hospitals using contrast-enhanced T1 sequences. The images were normalized using one of five investigated approaches including grey-level discretization, histogram matching and z-score. Then, radiomic features were extracted and features stability was evaluated using intra-class correlation coefficients. In the second part of the study, improvement in the prognostic performance of features was tested on 60 patients derived from publicly available dataset. Results Depending on the normalization scheme, the percentage of stable features varied from 3.4% to 8%. The histogram matching based on the tumor region showed the highest amount of the stable features (113/1404); while normalization using fixed bin size resulted in 48 stable features. The histogram matching also led to better prognostic value (median c-index increase of 0.065) comparing to non-normalized images. Conclusions MRI normalization plays an important role in radiomics. Appropriate normalization helps to select robust features, which can be used for prognostic modelling in multicenter studies. In our study, histogram matching based on tumor region improved both stability of radiomic features and their prognostic value

Interchangeability of radiomic features between [18F]-FDG PET/CT and [18F]-FDG PET/MR

PURPOSE Radiomics is a promising tool for identification of new prognostic biomarkers. However, image reconstruction settings and test-retest variability may influence the absolute values of radiomic features. Unstable radiomic features cannot be used as reliable biomarkers. PET/MR is becoming increasingly available and often replaces PET/CT for different indications. The aim of this study was to quantify to what extend [18F]-FDG PET/CT radiomics models can be transferred to [18F]-FDG PET/MR and thereby to investigate the feasibility of combined PET/CT-PET/MR models. For this purpose, we compared PET radiomic features calculated on PET/MR and PET/CT and on a 4D gated PET/MR dataset to select radiomic features that are robust to attenuation correction differences and test-retest variability, respectively. METHODS Two cohorts of patients with lung lesions were studied. In the first cohort (n=10), inhale and exhale phases of a 4D [18F]-FDG PET/MR (4DPETMR) scan were used as a surrogate for a test-retest dataset. In the second cohort (n=9), patients underwent first an [18F]-FDG PET/MR scan (SIGNA PET/MR, GE Healthcare, Waukesha) followed by an [18F]-FDG PET/CT scan (Discovery 690, GE Healthcare) with a delay of 33 min ± 5 min (PETCT-PETMR). Lesions were segmented on inhale and exhale 4D-PET phases and on the individual PET scans from PET/CT and PET/MR with two semi-automated methods (gradient-based and threshold-based). The scan resolution was 2.73x2.73x3.27 mm and 2.34x2.34x2.78 mm for the PET/CT and PET/MR, respectively. In total, 1355 radiomic features were calculated, i.e. shape (n=18), intensity (n=17), texture (n=136) and wavelet (n=1184). The intra-class correlation coefficient (ICC) was calculated to compare the radiomic features of the 4DPETMR (ICC(1,1)) and PETCT-PETMR (ICC(3,1)) datasets. An ICC>0.9 was considered stable among both types of PET scans. RESULTS AND CONCLUSION 4DPETMR showed highest stability for shape, intensity and texture (>80%) and lower stability for wavelet features (40%). Gradient-based method showed higher stability compared to threshold-based method except from shape features. In PETCT-PETMR, more than 61% of shape and intensity features were stable for both segmentation methods. However, a reduced stability was observed for texture (50%) and wavelet (<30%) features. More wavelet features were robust in the smoothed images (low-pass filtering) compared to images with emphasized heterogeneity (high-pass filtering). Comparing stable features of both investigations, highest agreement was found for intensity and lower agreement for shape, texture and wavelet features. Only 53.6% of stable texture features in 4DPETMR were also stable in PETCT-PETMR, and even less in case of wavelet features (40.4%). Approximately 16.9% (texture) and 43.2% (wavelet) of stable PETCT-PETMR features are unstable in 4DPETMR. To conclude, shape and intensity features were robust when comparing two types of [18F]-FDG PET scans (PET/CT and PET/MR). Reduced stability was observed for texture and wavelet features. We identified multiple origins of instability of radiomic features, such as attenuation correction differences, different uptake times and spatial resolution. This needs to be considered when models based on PET/CT are transferred PET/MR models or when combined models are used. This article is protected by copyright. All rights reserved

Transferability of radiomic signatures from experimental to human interstitial lung disease

BACKGROUND Interstitial lung disease (ILD) defines a group of parenchymal lung disorders, characterized by fibrosis as their common final pathophysiological stage. To improve diagnosis and treatment of ILD, there is a need for repetitive non-invasive characterization of lung tissue by quantitative parameters. In this study, we investigated whether CT image patterns found in mice with bleomycin induced lung fibrosis can be translated as prognostic factors to human patients diagnosed with ILD. METHODS Bleomycin was used to induce lung fibrosis in mice (n_control = 36, n_experimental = 55). The patient cohort consisted of 98 systemic sclerosis (SSc) patients (n_ILD = 65). Radiomic features (n_histogram = 17, n_texture = 137) were extracted from microCT (mice) and HRCT (patients) images. Predictive performance of the models was evaluated with the area under the receiver-operating characteristic curve (AUC). First, predictive performance of individual features was examined and compared between murine and patient data sets. Second, multivariate models predicting ILD were trained on murine data and tested on patient data. Additionally, the models were reoptimized on patient data to reduce the influence of the domain shift on the performance scores. RESULTS Predictive power of individual features in terms of AUC was highly correlated between mice and patients (r = 0.86). A model based only on mean image intensity in the lung scored AUC = 0.921 ± 0.048 in mice and AUC = 0.774 (CI95% 0.677-0.859) in patients. The best radiomic model based on three radiomic features scored AUC = 0.994 ± 0.013 in mice and validated with AUC = 0.832 (CI95% 0.745-0.907) in patients. However, reoptimization of the model weights in the patient cohort allowed to increase the model's performance to AUC = 0.912 ± 0.058. CONCLUSION Radiomic signatures of experimental ILD derived from microCT scans translated to HRCT of humans with SSc-ILD. We showed that the experimental model of BLM-induced ILD is a promising system to test radiomic models for later application and validation in human cohorts

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

ackground 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

PET/CT radiomics for prediction of hyperprogression in metastatic melanoma patients treated with immune checkpoint inhibitors

PurposeThis study evaluated pretreatment 2[18F]fluoro-2-deoxy-D-glucose (FDG)-PET/CT-based radiomic signatures for prediction of hyperprogression in metastatic melanoma patients treated with immune checkpoint inhibition (ICI).Material and methodFifty-six consecutive metastatic melanoma patients treated with ICI and available imaging were included in the study and 330 metastatic lesions were individually, fully segmented on pre-treatment CT and FDG-PET imaging. Lesion hyperprogression (HPL) was defined as lesion progression according to RECIST 1.1 and doubling of tumor growth rate. Patient hyperprogression (PD-HPD) was defined as progressive disease (PD) according to RECIST 1.1 and presence of at least one HPL. Patient survival was evaluated with Kaplan-Meier curves. Mortality risk of PD-HPD status was assessed by estimation of hazard ratio (HR). Furthermore, we assessed with Fisher test and Mann-Whitney U test if demographic or treatment parameters were different between PD-HPD and the remaining patients. Pre-treatment PET/CT-based radiomic signatures were used to build models predicting HPL at three months after start of treatment. The models were internally validated with nested cross-validation. The performance metric was the area under receiver operating characteristic curve (AUC).ResultsPD-HPD patients constituted 57.1% of all PD patients. PD-HPD was negatively related to patient overall survival with HR=8.52 (95%CI 3.47-20.94). Sixty-nine lesions (20.9%) were identified as progressing at 3 months. Twenty-nine of these lesions were classified as hyperprogressive, thereby showing a HPL rate of 8.8%. CT-based, PET-based, and PET/CT-based models predicting HPL at three months after the start of treatment achieved testing AUC of 0.703 +/- 0.054, 0.516 +/- 0.061, and 0.704 +/- 0.070, respectively. The best performing models relied mostly on CT-based histogram features.ConclusionsFDG-PET/CT-based radiomic signatures yield potential for pretreatment prediction of lesion hyperprogression, which may contribute to reducing the risk of delayed treatment adaptation in metastatic melanoma patients treated with ICI

Improved Survival Prediction by Combining Radiological Imaging and S-100B Levels Into a Multivariate Model in Metastatic Melanoma Patients Treated With Immune Checkpoint Inhibition

Purpose: We explored imaging and blood bio-markers for survival prediction in a cohort of patients with metastatic melanoma treated with immune checkpoint inhibition. Materials and Methods: 94 consecutive metastatic melanoma patients treated with immune checkpoint inhibition were included into this study. PET/CT imaging was available at baseline (Tp0), 3 months (Tp1) and 6 months (Tp2) after start of immunotherapy. Radiological response at Tp2 was evaluated using iRECIST. Total tumor burden (TB) at each time-point was measured and relative change of TB compared to baseline was calculated. LDH, CRP and S-100B were also analyzed. Cox proportional hazards model and logistic regression were used for survival analysis. Results: iRECIST at Tp2 was significantly associated with overall survival (OS) with C-index=0.68. TB at baseline was not associated with OS, whereas TB at Tp1 and Tp2 provided similar predictive power with C-index of 0.67 and 0.71, respectively. Appearance of new metastatic lesions during follow-up was an independent prognostic factor (C-index=0.73). Elevated LDH and S-100B ratios at Tp2 were significantly associated with worse OS: C-index=0.73 for LDH and 0.73 for S-100B. Correlation of LDH with TB was weak (r=0.34). A multivariate model including TB change, S-100B, and appearance of new lesions showed the best predictive performance with C-index=0.83. Conclusion: Our analysis shows only a weak correlation between LDH and TB. Additionally, baseline TB was not a prognostic factor in our cohort. A multivariate model combining early blood and imaging biomarkers achieved the best predictive power with regard to survival, outperforming iRECIST

Quantification of Inter- and Intra-Tumor Heterogeneity Using Medical Imaging and Its Implication on Response to Radiotherapy in Head and Neck Cancer

Cancer is a heterogeneous disease, showing intra- and inter-tumor genetic and phenotypic variability [1]. This variability translates to differential radiosensitivity and in consequence differential response to radiotherapy. Head and neck squamous cell carcinoma (HNSCC) accounts for around 5-10% of new cancer cases in developed countries [2]. It shows a heterogeneous response to radiochemotherapy with loco-regional control and 5 years overall survival ranging from below 50% to 80%. Few molecular factors were linked to outcome prognosis in HNSCC, for example human papilloma virus (HPV) infection. However, tissue-based biomarkers from tumor biopsies may not account for intra-tumor heterogeneity [3, 4]. This PhD project aims to identify new tumor phenotypes in HNSCC related to worse prognosis of treatment outcome using medical imaging techniques, which provides a 3D surrogate of tumor biology. Tumor density, metabolism and perfusion were studied in respect to different HNSCC subtypes and radiochemotherapy outcome. A quantitative and comprehensive image analysis method, radiomics, was used to link intra-tumor heterogeneity and treatment outcome. Radiomics comprises four types of descriptors: shape, intensity, texture and filter-based. It not only quantifies general properties of a tumor, for example higher metabolic activity, but also provides information about the intra-tumor heterogeneity. In the first subproject, I have analyzed tumor perfusion, metabolism and their correlation in subgroups of HNSCC based on: tumor subtype (oropharynx, hypopharynx, larynx and oral cavity), tumor stage (T1/T2 vs T3/T4) and HPV status. Computed tomography perfusion (CTP) and 18F-fludeoxyglucose positron emission tomography (18F-FDG PET), from 41 HNSCC patients were analyzed. Three perfusion parameters: blood volume (BV), blood flow (BF) and mean transit time (MTT), were computed. Difference in perfusion parameters between the gross tumor volume (GTV) and its surrounding tissue were investigated. Tumor subgroups related to worse prognosis (T3/T4 and HPV negative) showed increased BV and MTT in comparison to surrounding healthy tissue. Additionally, I have shown that the correlation of FDG uptake and perfusion is tumor subgroup dependent. I have observed positive correlation only for HPV positive (r = 0.86, p = 0.04) and oropharyngeal (r=0.63, p = 0.05) cancer. CTP consists of repeated CT scans and is thus dose intensive. I have performed a separate study using Alderson phantom to adapt our clinical CTP head and neck protocol. The endpoint was a decrease in delivered dose and maintenance of image quality. Our standard protocol on GE revolution CT is 100 kV, 80 mAs, 5 mm slice thickness and filtered back projection algorithm. I have adapted the percentage of an adaptive statistical iterative reconstruction (ASiR), slice thickness, tube current and voltage. The signal to noise ratio was measured in 7 predefined regions of interest and the effective dose was estimated using thermoluminescent dosimeters. The optimized protocol used 80 kV, a tube current adapted based on anatomy from 15 to 80 mAs, 2.5 mm slice thickness and 50% of ASiR reconstruction. The effective dose was decreased by factor of 2 whereas the image quality was maintained. In the second part of the thesis, I have investigated radiomics for its ability to predict treatment outcome and its correlation to tumor biology. An in-house radiomics software implementation was developed in Python programing language (v 2.7). Most of the radiomics studies are performed using in-house implementations or open source codes and the implemented workflows are currently not fully standardized. Therefore, I have validated my implementation against implementation from MAASTRO clinic, Maastricht, the Netherlands. I have also used both implementations to train local tumor control models based on 18F-FDG PET imaging 3 months post-radiochemotherapy (128 patients). Only 80 out of 649 radiomic features, available in both implementations and based on the same mathematical definition, were reproducible between the implementations (intraclass correlation coefficient ICC > 0.8). In the univariate Cox regression feature’s prognostic power depended strongly on used implementation. The main causes of irreproducibility were differences in contour mask creation, translation of bin size to filtered images, and type of the used transform decimated vs undecimated wavelet transform. In another radiomics robustness study I have investigated the stability of radiomic features in respect to different CTP calculation factors. Some of the CTP calculation factors are difficult to standardize (arterial input function definition and noise threshold in the calculation) and thus should be considered before linking CTP radiomics with clinical outcome. I have analyzed CTP scans in lung (n = 11) and head and neck cancer (n = 11). 255 out of 945 CTP radiomic features were stable in both tumor sites in respect to artery contouring and noise threshold. Among them, I have identified 10 groups of radiomic features, after the correction for inter-features correlations and correlation to tumor volume. These features should be further tested for their prognostic power. In the prognostic modeling, I have investigated the link between local tumor control and radiomics in HNSCC based on contrast-enhanced CT and 18F-FDG PET pre-treatment imaging. I have used two cohorts of patients: retrospective for models training (n > 90 patients) and prospective cohort from institutional phase II study with a standardized imaging protocol for models validation (n > 50 patients). I have observed that tumors more heterogeneous in CT density were at higher risk for tumor recurrence. This model had a higher prognostic power than model incorporating clinical prognostic factors (tumor stage, volume and HPV status) or combination of CT radiomics and clinical factors, concordance index (CI) in the validation cohort CIradiomics = 0.78, CIclinical = 0.73 and CIcombination = 0.76. In a follow-up study, I have investigated whether the inclusion of metabolic information can further improve radiomics-based local tumor control modeling. I have observed that round tumors (based on 18-FDG PET autosegmentation) with a focused region of high FDG uptake surrounded by a rim of low FDG uptake were linked with better prognosis. However, this model did not outperform the CT based model. In the validation cohort evaluated in this study, both models achieved CI around 0.7. Also the combination of PET and CT radiomics did not improve the predictions. Nevertheless, the PET radiomics model showed a better calibration, which may be linked to the presence of metal artifacts in CT in head and neck region. To link the abstract radiomic features with tumor biology, I have correlated CT radiomics with HPV status. I have observed that tumors more homogenous in CT density tend to be HPV positive. Although, this signature (set of radiomic features) has a similar interpretation to local tumor control signature, it comprised different features and the signatures were not correlated with each other. For example local tumor control CT radiomics model was also prognostic in HPV negative subgroup of patients. In summary, I have shown that biological information can be recovered even from simple morphological imaging (CT). Additionally, I have identified imaging signatures, based on differences in perfusion between tumor and its surrounding as well as CT and PET radiomics, which were linked with worse outcome prognosis. These signatures need to be further validated in an external cohort of patients and treatment intensification options for worse prognosis groups have to be defined