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

Purpose

This 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 method

Fifty-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).

Results

PD-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.

Conclusions

FDG-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.

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.

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.

Long-term outcomes of operable stage III NSCLC in the pre-immunotherapy era: results from a pooled analysis of the SAKK 16/96, SAKK 16/00, SAKK 16/01, and SAKK 16/08 trials

Background

Chemoradiotherapy with durvalumab consolidation has yielded excellent results in stage III non-small-cell lung cancer (NSCLC). Therefore, it is essential to identify patients who might benefit from a surgical approach.

Material and methods

Data from 437 patients with operable stage III NSCLC enrolled in four consecutive Swiss Group for Clinical Cancer Research (SAKK) trials (16/96, 16/00, 16/01, 16/08) were pooled and outcomes were analyzed in 431 eligible patients. All patients were treated with three cycles of induction chemotherapy (cisplatin/docetaxel), followed in some patients by neoadjuvant radiotherapy (44 Gy, 22 fractions) (16/00, 16/01, 16/08) and cetuximab (16/08).

Results

With a median follow-up time of 9.3 years (range 8.5-10.3 years), 5- and 10-year overall survival (OS) rates were 37% and 25%, respectively. Overall, 342 patients (79%) underwent tumor resection, with a complete resection (R0) rate of 80%. Patients (n = 272, 63%) with R0 had significantly longer OS compared to patients who had surgery but incomplete resection (64.8 versus 19.2 months, P < 0.001). OS for patients who achieved pathological complete remission (pCR) (n = 66, 15%) was significantly better compared to resected patients without pCR (86.5 versus 37.0 months, P = 0.003). For patients with pCR, the 5- and 10-year event-free survival and OS rates were 45.7% [95% confidence interval (CI) 32.8% to 57.7%] and 28.1% (95% CI 15.2% to 42.6%), and 58.2% (95% CI 45.2% to 69.2%) and 45.0% (95% CI 31.5% to 57.6%), respectively.

Conclusion

We report favorable long-term outcomes in patients with operable stage III NSCLC treated with neoadjuvant chemotherapy with cisplatin and docetaxel ± neoadjuvant sequential radiotherapy from four prospective SAKK trials. Almost two-third of the patients underwent complete resection after neoadjuvant therapy. We confirm R0 resection and pCR as important predictors of outcome.

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Combined modality treatment in operable stage III NSCLC results in 5- and 10-year survival rates of 37% and 25%.

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Long-term survival for patients with incomplete resection is poor.

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Complete resection and pCR are important predictors for outcome.

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.

Preselection of robust radiomic features does not improve outcome modelling in non-small cell lung cancer based on clinical routine FDG-PET imaging

Background

Radiomics is a promising tool for identifying imaging-based biomarkers. Radiomics-based models are often trained on single-institution datasets; however, multi-centre imaging datasets are preferred for external generalizability owing to the influence of inter-institutional scanning differences and acquisition settings. The study aim was to determine the value of preselection of robust radiomic features in routine clinical positron emission tomography (PET) images to predict clinical outcomes in locally advanced non-small cell lung cancer (NSCLC).

Methods

A total of 1404 primary tumour radiomic features were extracted from pre-treatment [¹⁸F]fluorodeoxyglucose (FDG)-PET scans of stage IIIA/N2 or IIIB NSCLC patients using a training cohort (n = 79; prospective Swiss multi-centre randomized phase III trial SAKK 16/00; 16 centres) and an internal validation cohort (n = 31; single centre). Robustness studies investigating delineation variation, attenuation correction and motion were performed (intraclass correlation coefficient threshold > 0.9). Two 12-/24-month event-free survival (EFS) and overall survival (OS) logistic regression models were trained using standardized imaging: (1) with robust features alone and (2) with all available features. Models were then validated using fivefold cross-validation, and validation on a separate single-centre dataset. Model performance was assessed using area under the receiver operating characteristic curve (AUC).

Results

Robustness studies identified 179 stable features (13%), with 25% stable features for 3D versus 4D acquisition, 31% for attenuation correction and 78% for delineation. Univariable analysis found no significant robust features predicting 12-/24-month EFS and 12-month OS (p value > 0.076). Prognostic models without robust preselection performed well for 12-month EFS in training (AUC = 0.73) and validation (AUC = 0.74). Patient stratification into two risk groups based on 12-month EFS was significant for training (p value = 0.02) and validation cohorts (p value = 0.03).

Conclusions

A PET-based radiomics model using a standardized, multi-centre dataset to predict EFS in locally advanced NSCLC was successfully established and validated with good performance. Prediction models with robust feature preselection were unsuccessful, indicating the need for a standardized imaging protocol.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13550-021-00809-3.

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.

Radiomic Analysis to Predict Outcome in Recurrent Glioblastoma Based on Multi-Center MR Imaging From the Prospective DIRECTOR Trial

Background

Based on promising results from radiomic approaches to predict O⁶-methylguanine DNA methyltransferase promoter methylation status (MGMT status) and clinical outcome in patients with newly diagnosed glioblastoma, the current study aimed to evaluate radiomics in recurrent glioblastoma patients.

Methods

Pre-treatment MR-imaging data of 69 patients enrolled into the DIRECTOR trial in recurrent glioblastoma served as a training cohort, and 49 independent patients formed an external validation cohort. Contrast-enhancing tumor and peritumoral volumes were segmented on MR images. 180 radiomic features were extracted after application of two MR intensity normalization techniques: fixed number of bins and linear rescaling. Radiomic feature selection was performed via principal component analysis, and multivariable models were trained to predict MGMT status, progression-free survival from first salvage therapy, referred to herein as PFS₂, and overall survival (OS). The prognostic power of models was quantified with concordance index (CI) for survival data and area under receiver operating characteristic curve (AUC) for the MGMT status.

Results

We established and validated a radiomic model to predict MGMT status using linear intensity interpolation and considering features extracted from gadolinium-enhanced T1-weighted MRI (training AUC = 0.670, validation AUC = 0.673). Additionally, models predicting PFS₂ and OS were found for the training cohort but were not confirmed in our validation cohort.

Conclusions

A radiomic model for prediction of MGMT promoter methylation status from tumor texture features in patients with recurrent glioblastoma was successfully established, providing a non-invasive approach to anticipate patient's response to chemotherapy if biopsy cannot be performed. The radiomic approach to predict PFS₂ and OS failed.

Impact of CT convolution kernel on robustness of radiomic features for different lung diseases and tissue types

OBJECTIVES

In this study, we aimed to assess the impact of different CT reconstruction kernels on the stability of radiomic features and the transferability between different diseases and tissue types. Three lung diseases were evaluated, i.e. non-small cell lung cancer (NSCLC), malignant pleural mesothelioma (MPM) and interstitial lung disease related to systemic sclerosis (SSc-ILD) as well as four different tissue types, i.e. primary tumor, largest involved lymph node ipsilateral and contralateral lung.

METHODS

Pre-treatment non-contrast enhanced CT scans from 23 NSCLC, 10 MPM and 12 SSc-ILD patients were collected retrospectively. For each patient, CT scans were reconstructed using smooth and sharp kernel in filtered back projection. The regions of interest (ROIs) were contoured on the smooth kernel-based CT and transferred to the sharp kernel-based CT. The voxels were resized to the largest voxel dimension of each cohort. In total, 1386 features were analyzed. Feature stability was assessed using the intraclass correlation coefficient. Features above the stability threshold >0.9 were considered stable.

RESULTS

We observed a strong impact of the reconstruction method on stability of the features (at maximum 26% of the 1386 features were stable). Intensity features were the most stable followed by texture and wavelet features. The wavelet features showed a positive correlation between percentage of stable features and size of the ROI (R2 = 0.79, p = 0.005). Lymph node radiomics showed poorest stability (<10%) and lung radiomics the largest stability (26%). Robustness analysis done on the contralateral lung could to a large extent be transferred to the ipsilateral lung, and the overlap of stable lung features between different lung diseases was more than 50%. However, results of robustness studies cannot be transferred between tissue types, which was investigated in NSCLC and MPM patients; the overlap of stable features for lymph node and lung, as well as for primary tumor and lymph node was very small in both disease types.

CONCLUSION

The robustness of radiomic features is strongly affected by different reconstruction kernels. The effect is largely influenced by the tissue type and less by the disease type.

ADVANCES IN KNOWLEDGE

The study presents to our knowledge the most complete analysis on the impact of convolution kernel on the robustness of CT-based radiomics for four relevant tissue types in three different lung diseases. .

Radiomics Feature Activation Maps as a New Tool for Signature Interpretability

Introduction

In the field of personalized medicine, radiomics has shown its potential to support treatment decisions. However, the limited feature interpretability hampers its introduction into the clinics. Here, we propose a new methodology to create radiomics feature activation maps, which allows to identify the spatial-anatomical locations responsible for signature activation based on local radiomics. The feasibility of this technique will be studied for histological subtype differentiation (adenocarcinoma versus squamous cell carcinoma) in non-small cell lung cancer (NSCLC) using computed tomography (CT) radiomics.

Materials and Methods

Pre-treatment CT scans were collected from a multi-centric Swiss trial (training, n=73, IIIA/N2 NSCLC, SAKK 16/00) and an independent cohort (validation, n=32, IIIA/N2/IIIB NSCLC). Based on the gross tumor volume (GTV), four peritumoral region of interests (ROI) were defined: lung_exterior (expansion into the lung), iso_exterior (expansion into lung and soft tissue), gradient (GTV border region), GTV+Rim (GTV and iso_exterior). For each ROI, 154 radiomic features were extracted using an in-house developed software implementation (Z-Rad, Python v2.7.14). Features robust against delineation variability served as an input for a multivariate logistic regression analysis. Model performance was quantified using the area under the receiver operating characteristic curve (AUC) and verified using five-fold cross validation and internal validation. Local radiomic features were extracted from the GTV+Rim ROI using non-overlapping 3x3x3 voxel patches previously marked as GTV or rim. A binary activation map was created for each patient using the median global feature value from the training. The ratios of activated/non-activated patches of GTV and rim regions were compared between histological subtypes (Wilcoxon test).

Results

Iso_exterior, gradient, GTV+Rim showed good performances for histological subtype prediction (AUC_training=0.68-0.72 and AUC_validation=0.73-0.74) whereas GTV and lung_exterior models failed validation. GTV+Rim model feature activation maps showed that local texture feature distribution differed significantly between histological subtypes in the rim (p=0.0481) but not in the GTV (p=0.461).

Conclusion

In this exploratory study, radiomics-based prediction of NSCLC histological subtypes was predominantly based on the peritumoral region indicating that radiomics activation maps can be useful for tracing back the spatial location of regions responsible for signature activation.

Dosimetric and geometric end-to-end accuracy of a magnetic resonance guided linear accelerator

The introduction of real-time imaging by magnetic resonance guided linear accelerators (MR-Linacs) enabled adaptive treatments and gating on the tumor position. Different end-to-end tests monitored the accuracy of our MR-Linac during the first year of clinical operation. We report on the stability of these tests covering a static, adaptive and gating workflow. Film measurements showed gamma passing rates of 96.4% ± 3.4% for the static tests (five measurements) and for the two adaptive tests 98.9% and 99.99%, respectively (criterion 2%/2mm). The gated point dose measurements in the breathing phantom were 2.7% lower than in the static phantom.

FDG PET versus CT radiomics to predict outcome in malignant pleural mesothelioma patients

BACKGROUND

Careful selection of malignant pleural mesothelioma (MPM) patients for curative treatment is of highest importance, as the multimodal treatment regimen is challenging for patients and harbors a high risk of substantial toxicity. Radiomics-a quantitative method for image analysis-has shown its prognostic ability in different tumor entities and could therefore play an important role in optimizing patient selection for radical cancer treatment. So far, radiomics as a prognostic tool in MPM was not investigated.

MATERIALS AND METHODS

This study is based on 72 MPM patients treated with surgery in a curative intent at our institution between 2009 and 2017. Pre-treatment Fluorine-18 fluorodeoxyglucose (FDG) PET and CT scans were used for radiomics outcome modeling. After extraction of 1404 CT and 1410 FDG PET features from each image, a preselection by principal component analysis was performed to include only robust, non-redundant features for the cox regression to predict the progression-free survival (PFS) and the overall survival (OS). Results were validated on a separate cohort. Additionally, SUVmax and SUVmean, and volume were tested for their prognostic ability for PFS and OS.

RESULTS

For the PFS a concordance index (c-index) of 0.67 (95% CI 0.52-0.82) and 0.66 (95% CI 0.57-0.78) for the training cohort (n = 36) and internal validation cohort (n = 36), respectively, were obtained for the PET radiomics model. The PFS advantage of the low-risk group translated also into an OS advantage. On CT images, no radiomics model could be trained. SUV max and SUV mean were also not prognostic in terms of PFS and OS.

CONCLUSION

We were able to build a successful FDG PET radiomics model for the prediction of PFS in MPM. Radiomics could serve as a tool to aid clinical decision support systems for treatment of MPM in future.

Radiomics, Tumor Volume, and Blood Biomarkers for Early Prediction of Pseudoprogression in Patients with Metastatic Melanoma Treated with Immune Checkpoint Inhibition

PURPOSE

We assessed the predictive potential of positron emission tomography (PET)/CT-based radiomics, lesion volume, and routine blood markers for early differentiation of pseudoprogression from true progression at 3 months.

EXPERIMENTAL DESIGN

112 patients with metastatic melanoma treated with immune checkpoint inhibition were included in our study. Median follow-up duration was 22 months. 716 metastases were segmented individually on CT and 2[18F]fluoro-2-deoxy-D-glucose (FDG)-PET imaging at three timepoints: baseline (TP0), 3 months (TP1), and 6 months (TP2). Response was defined on a lesion-individual level (RECIST 1.1) and retrospectively correlated with FDG-PET/CT radiomic features and the blood markers LDH/S100. Seven multivariate prediction model classes were generated.

RESULTS

Two-year (median) overall survival, progression-free survival, and immune progression-free survival were 69% (not reached), 24% (6 months), and 42% (16 months), respectively. At 3 months, 106 (16%) lesions had progressed, of which 30 (5%) were identified as pseudoprogression at 6 months. Patients with pseudoprogressive lesions and without true progressive lesions had a similar outcome to responding patients and a significantly better 2-year overall survival of 100% (30 months), compared with 15% (10 months) in patients with true progressions/without pseudoprogression (P = 0.002). Patients with mixed progressive/pseudoprogressive lesions were in between at 53% (25 months). The blood prediction model (LDH+S100) achieved an AUC = 0.71. Higher LDH/S100 values indicated a low chance of pseudoprogression. Volume-based models: AUC = 0.72 (TP1) and AUC = 0.80 (delta-volume between TP0/TP1). Radiomics models (including/excluding volume-related features): AUC = 0.79/0.78. Combined blood/volume model: AUC = 0.79. Combined blood/radiomics model (including volume-related features): AUC = 0.78. The combined blood/radiomics model (excluding volume-related features) performed best: AUC = 0.82.

CONCLUSIONS

Noninvasive PET/CT-based radiomics, especially in combination with blood parameters, are promising biomarkers for early differentiation of pseudoprogression, potentially avoiding added toxicity or delayed treatment switch.

Benchmarking Monte-Carlo dose calculation for MLC CyberKnife treatments

Background

Vendor-independent Monte Carlo (MC) dose calculation (IDC) for patient-specific quality assurance of multi-leaf collimator (MLC) based CyberKnife treatments is used to benchmark and validate the commercial MC dose calculation engine for MLC based treatments built into the CyberKnife treatment planning system (Precision MC).

Methods

The benchmark included dose profiles in water in 15 mm depth and depth dose curves of rectangular MLC shaped fields ranging from 7.6 mm × 7.7 mm to 115.0 mm  × 100.1 mm, which were compared between IDC, Precision MC and measurements in terms of dose difference and distance to agreement. Dose distributions of three phantom cases and seven clinical lung cases were calculated using both IDC and Precision MC. The lung PTVs ranged from 14 cm³ to 93 cm³. Quantitative comparison of these dose distributions was performed using dose-volume parameters and 3D gamma analysis with 2% global dose difference and 1 mm distance criteria and a global 10% dose threshold. Time to calculate dose distributions was recorded and efficiency was assessed.

Results

Absolute dose profiles in 15 mm depth in water showed agreement between Precision MC and IDC within 3.1% or 1 mm. Depth dose curves agreed within 2.3% / 1 mm. For the phantom and clinical lung cases, mean PTV doses differed from − 1.0 to + 2.3% between IDC and Precision MC and gamma passing rates were > =98.1% for all multiple beam treatment plans. For the lung cases, lung V20 agreed within ±1.5%. Calculation times ranged from 2.2 min (for 39 cm³ PTV at 1.0 × 1.0 × 2.5 mm³ native CT resolution) to 8.1 min (93 cm³ at 1.1 × 1.1 × 1.0 mm³), at 2% uncertainty for Precision MC for the 7 examined lung cases and 4–6 h for IDC, which, however, is not optimized for efficiency but used as a gold standard for accuracy.

Conclusions

Both accuracy and efficiency of Precision MC in the context of MLC based planning for the CyberKnife M6 system were benchmarked against MC based IDC framework. Precision MC is used in clinical practice at our institute.

CT radiomics and PET radiomics: ready for clinical implementation?

INTRODUCTION

Today, rapid technical and clinical developments result in an increasing number of treatment options for oncological diseases. Thus, decision support systems are needed to offer the right treatment to the right patient. Imaging biomarkers hold great promise in patient-individual treatment guidance. Routinely performed for diagnosis and staging, imaging datasets are expected to hold more information than used in the clinical practice. Radiomics describes the extraction of a large number of meaningful quantitative features from medical images, such as computed tomography (CT) and positron emission tomography (PET). Due to the non-invasive nature and ability to capture 3D image-based heterogeneity, radiomic features are potential surrogate markers of the cancer phenotype. Several radiomic studies are published per day, owing to encouraging results of many radiomics-based patient outcome models. Despite this comparably large number of studies, radiomics is mainly studied in proof of principle concept. Hence, a translation of radiomics from a hot topic research field into an essential clinical decision-making tool is lacking, but of high clinical interest.

EVIDENCE ACQUISITION

Herein, we present a literature review addressing the clinical evidence of CT and PET radiomics. An extensive literature review was conducted in PubMed, including papers on robustness and clinical applications.

EVIDENCE SYNTHESIS

We summarize image-modality related influences on the robustness of radiomic features and provide an overview of clinical evidence reported in the literature. Today, more evidence has been provided for CT imaging, however, PET imaging offers the promise of direct imaging of biological processes and functions. We provide a summary of future research directions, which needs to be addressed in order to successfully introduce radiomics into clinical medicine. In comparison to CT, more focus should be directed towards harmonization of PET acquisition and reconstruction protocols, which is important for transferable modelling.

CONCLUSIONS

Both CT and PET radiomics are promising pre-treatment and intra-treatment biomarkers for outcome prediction. Most studies are performed in retrospective setting, however their validation in prospective data collections is ongoing.

Delta-radiomics for prediction of pseudoprogression in malignant melanoma treated with immune checkpoint inhibition

9575

Background: Distinguishing progressive disease (PD) from pseudoprogression (PP) in patients treated with immune-checkpoint inhibition (ICI) is challenging and usually requires confirmation follow-up imaging or invasive diagnostic techniques. This project aimed to identify predictive radiomic signatures for PP from CT imaging. Methods: The response to ICI of 105 metastatic melanoma patients with 645 metastases was retrospectively correlated with radiomic signatures (172 total features). All metastatic lesions were delineated at 3 time points: prior to ICI (t0), at 3 (t1) and 6 months (t2). Response was defined individually for each metastasis using RECIST 1.1, comparing baseline t0 to t2. Three prediction models for PP were built: CT radiomics at t0 and t1, as well as the relative difference between both t0 and t1 (delta-radiomics). Results: Median follow-up was 18 months and 2-year OS and PFS were 72% and 25%, respectively. Median OS: not reached, median PFS: 6 months. Response per lesion at t1: 13% complete remission (CR), 19% partial remission (PR), 52% stable disease (SD) and 16% PD. At t2: 16% CR, 31% PR, 38% SD and 15% PD. 106 progressive lesions were identified at t1, of which, 26 changed to SD, 1 to CR and 3 to PR at t2, resulting in 30 PPs (4.7%). Metastasis location significantly influenced response rates but was not associated with PP (p = 0.4). Lung metastases had significantly higher response rates than soft tissue (p < 0.001), liver (p < 0.001) and bone metastases (p = 0.008). Univariate analysis followed by removal of correlated features revealed no significant radiomic features associated with PP at t0. One independent feature was identified at t1 (AUC 0.74), while delta-radiomics was the best performing approach, identifying four independent features (AUC 0.72 to 0.81). A final multivariate delta radiomics logistic regression model was generated and internally validated, achieving an AUC of 0.81 (± 0.11, 10-fold cross-validation). Conclusions: Metastasis location significantly influenced response rates and CT-based delta-radiomics is a promising biomarker for early differentiation between pseudoprogression and true progression in metastatic melanoma patients treated with ICI.

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 ± 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 2.73 × 2.73 × 3.27 mm and 2.34 × 2.34 × 2.78 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 intraclass 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

The 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.

Onnamide F: a new nematocide from a southern Australian marine sponge, Trachycladus laevispirulifer

A southern Australian marine sponge, Trachycladus laevispirulifer, has yielded a potent new nematocide with antifungal activity which has been identified as onnamide F (1). The structure for 1 was assigned by detailed spectroscopic analysis and chemical conversion to the methyl ester 2. Onnamide F contains a common structural motif previously described in a number of natural products exhibiting interesting pharmacological activities, including the insect chemical defense agent pederin (3), and the sponge metabolites the onnamides, mycalamides, and theopederins.

Phorbasin A: a novel diterpene from a southern australian marine sponge, Phorbas species

A southern Australian Phorbas species has yielded a novel diterpene, phorbasin A (1), possessing an unprecedented carbon skeleton. The structure for phorbasin A was determined by detailed spectroscopic analysis.

Seabather's eruption

Seabather's eruption is an unusual rash that develops in individuals who have been swimming in the ocean. We report the case of a 25-year-old woman who had the rash in a typical bathing suit distribution. Several species of cnidarian larvae have been implicated in causing the disease. Symptomatic treatment is the mainstay of therapy for this self-limited rash. Preventive measures allow patients to avoid the disease altogether.