Prediction of soft tissue sarcoma response to radiotherapy using longitudinal diffusion MRI and a deep neural network with generative adversarial network-based data augmentation

The mitotic rate as a prognostic biomarker after preoperative radiotherapy for high-grade limb/trunk soft tissue sarcoma

PURPOSE

Currently there is no generally accepted standardized approach for the pathological evaluation of soft tissue sarcoma (STS) histology appearance after preoperative radiotherapy (PORT). This study aimed to investigate the prognostic value of pathological appearance after PORT for patients with high-grade limb/trunk STS.

METHODS

A cohort of 116 patients with high-grade STS of the limb/trunk treated with PORT followed by resection were evaluated. Patient characteristics, imaging tumor morphology (size, volume), and histopathology (mitotic and necrosis rate, viable cell, hyalinization/fibrosis cytopathic effect) were reviewed and reassessed. Disease free survival (DFS) and overall survival (OS) were calculated using the Kaplan-Meier method, and the hazard ratio was derived from Cox proportional hazard models. Two predictive nomograms were calculated based on significant predictors identified.

RESULTS

The 5-year DFS and OS were 52.9% and 70.3%, respectively. Tumor size before (HR:1.07, 95%CI: 1.01-1.14) and after PORT (HR:1.08, 95%CI: 1.01-1.14), tumor volume (HR:1.06, 95%CI: 1.01-1.12), mitotic rate after PORT (HR: 1.06, 95%CI: 1.02-1.11), mitotic rate change after PORT (HR:1.04, 95%CI:1.00-1.09) were independent risk factors for DFS. Tumor size before (HR:1.08, 95%CI: 1.03-1.14) and after PORT (HR:1.09, 95%CI: 1.04-1.15), tumor volume (HR:1.05, 95%CI: 1.01-1.09), mitotic rate after PORT (HR: 1.09, 95%CI: 1.04-1.13), mitotic rate change after PORT (HR:1.05, 95%CI:1.01-1.09) were independent risk factors for OS. The C-index of pathologic predictive nomogram based on mitotic rate for DFS and OS were 0.67 and 0.73, respectively. The C-index of morphology-pathology predictive nomogram for OS was 0.79.

CONCLUSION

Tumor size before and after PORT, tumor volume, mitotic rate after PORT, mitotic rate change after PORT were independent risk factors for DFS and OS in high-grade STS patients treated with PORT. The mitotic rate, independent of tumor morphology, showed its potential as a prognostic biomarker for pathologic evaluation in patients treated with PORT.

A multimodal neural network with gradient blending improves predictions of survival and metastasis in sarcoma

The objective of this study is to develop a multimodal neural network (MMNN) model that analyzes clinical variables and MRI images of a soft tissue sarcoma (STS) patient, to predict overall survival and risk of distant metastases. We compare the performance of this MMNN to models based on clinical variables alone, radiomics models, and an unimodal neural network. We include patients aged 18 or older with biopsy-proven STS who underwent primary resection between January 1st, 2005, and December 31st, 2020 with complete outcome data and a pre-treatment MRI with both a T1 post-contrast sequence and a T2 fat-sat sequence available. A total of 9380 MRI slices containing sarcomas from 287 patients are available. Our MMNN accepts the entire 3D sarcoma volume from T1 and T2 MRIs and clinical variables. Gradient blending allows the clinical and image sub-networks to optimally converge without overfitting. Heat maps were generated to visualize the salient image features. Our MMNN outperformed all other models in predicting overall survival and the risk of distant metastases. The C-Index of our MMNN for overall survival is 0.77 and the C-Index for risk of distant metastases is 0.70. The provided heat maps demonstrate areas of sarcomas deemed most salient for predictions. Our multimodal neural network with gradient blending improves predictions of overall survival and risk of distant metastases in patients with soft tissue sarcoma. Future work enabling accurate subtype-specific predictions will likely utilize similar end-to-end multimodal neural network architecture and require prospective curation of high-quality data, the inclusion of genomic data, and the involvement of multiple centers through federated learning.

The role of imaging in extremity sarcoma surgery

The surgical management of extremity bone and soft tissue sarcomas has evolved significantly over the last 50 years. The introduction and refinement of high-resolution cross-sectional imaging has allowed accurate assessment of anatomy and tumor extent, and in the current era more than 90% of patients can successfully undergo limb-salvage surgery. Advances in imaging have also revolutionized the clinician's ability to assess treatment response, detect metastatic disease, and perform intraoperative surgical navigation. This review summarizes the broad and essential role radiology plays in caring for sarcoma patients from diagnosis to post-treatment surveillance. Present evidence-based imaging paradigms are highlighted along with key future directions.

Longitudinal Image Data for Outcome Modeling

In oncology, medical imaging is crucial for diagnosis, treatment planning and therapy execution. Treatment responses can be complex and varied and are known to involve factors of treatment, patient characteristics and tumor microenvironment. Longitudinal image analysis is able to track temporal changes, aiding in disease monitoring, treatment evaluation, and outcome prediction. This allows for the enhancement of personalized medicine. However, analyzing longitudinal 2D and 3D images presents unique challenges, including image registration, reliable segmentation, dealing with variable imaging intervals, and sparse data. This review presents an overview of techniques and methodologies in longitudinal image analysis, with a primary focus on outcome modeling in radiation oncology.

Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO)

INTRODUCTION

The advent of image-guided radiation therapy (IGRT) has recently changed the workflow of radiation treatments by ensuring highly collimated treatments. Artificial intelligence (AI) and radiomics are tools that have shown promising results for diagnosis, treatment optimization and outcome prediction. This review aims to assess the impact of AI and radiomics on modern IGRT modalities in RT.

METHODS

A PubMed/MEDLINE and Embase systematic review was conducted to investigate the impact of radiomics and AI to modern IGRT modalities. The search strategy was "Radiomics" AND "Cone Beam Computed Tomography"; "Radiomics" AND "Magnetic Resonance guided Radiotherapy"; "Radiomics" AND "on board Magnetic Resonance Radiotherapy"; "Artificial Intelligence" AND "Cone Beam Computed Tomography"; "Artificial Intelligence" AND "Magnetic Resonance guided Radiotherapy"; "Artificial Intelligence" AND "on board Magnetic Resonance Radiotherapy" and only original articles up to 01.11.2022 were considered.

RESULTS

A total of 402 studies were obtained using the previously mentioned search strategy on PubMed and Embase. The analysis was performed on a total of 84 papers obtained following the complete selection process. Radiomics application to IGRT was analyzed in 23 papers, while a total 61 papers were focused on the impact of AI on IGRT techniques.

DISCUSSION

AI and radiomics seem to significantly impact IGRT in all the phases of RT workflow, even if the evidence in the literature is based on retrospective data. Further studies are needed to confirm these tools' potential and provide a stronger correlation with clinical outcomes and gold-standard treatment strategies.

Quantitative MRI on MR-Linacs: Towards Biological Image-Guided Adaptive Radiotherapy

Recognizing the potential of quantitative imaging biomarkers (QIBs) in radiotherapy, many studies have investigated the prognostic value of quantitative MRI (qMRI). With the introduction of MRI-guided radiotherapy systems, the practical challenges of repeated imaging have been substantially reduced. Since patients are treated inside an MRI scanner, acquisition of qMRI can be done during each fraction with limited or no prolongation of the fraction duration. In this review paper, we identify the steps that need been taken to move from MR as an imaging technique to a useful biomarker for MRI-guided radiotherapy (MRgRT).

Accurate, repeatable, and geometrically precise diffusion-weighted imaging on a 0.35 T magnetic resonance imaging-guided linear accelerator

Background and purpose

Diffusion weighted imaging (DWI) allows for the interrogation of tissue cellularity, which is a surrogate for cellular proliferation. Previous attempts to incorporate DWI into the workflow of a 0.35 T MR-linac (MRL) have lacked quantitative accuracy. In this study, accuracy, repeatability, and geometric precision of apparent diffusion coefficient (ADC) maps produced using an echo planar imaging (EPI)-based DWI protocol on the MRL system is illustrated, and in vivo potential for longitudinal patient imaging is demonstrated.

Materials and methods

Accuracy and repeatability were assessed by measuring ADC values in a diffusion phantom at three timepoints and comparing to reference ADC values. System-dependent geometric distortion was quantified by measuring the distance between 93 pairs of phantom features on ADC maps acquired on a 0.35 T MRL and a 3.0 T diagnostic scanner and comparing to spatially precise CT images. Additionally, for five sarcoma patients receiving radiotherapy on the MRL, same-day in vivo ADC maps were acquired on both systems, one of which at multiple timepoints.

Results

Phantom ADC quantification was accurate on the 0.35 T MRL with significant discrepancies only seen at high ADC. Average geometric distortions were 0.35 (±0.02) mm and 0.85 (±0.02) mm in the central slice and 0.66 (±0.04) mm and 2.14 (±0.07) mm at 5.4 cm off-center for the MRL and diagnostic system, respectively. In the sarcoma patients, a mean pretreatment ADC of 910x10-6 (±100x10-6) mm2/s was measured on the MRL.

Conclusions

The acquisition of accurate, repeatable, and geometrically precise ADC maps is possible at 0.35 T with an EPI approach.

Stereotactic Magnetic Resonance-Guided Adaptive and Non-Adaptive Radiotherapy on Combination MR-Linear Accelerators: Current Practice and Future Directions

Stereotactic body radiotherapy (SBRT) is an effective radiation therapy technique that has allowed for shorter treatment courses, as compared to conventionally dosed radiation therapy. As its name implies, SBRT relies on daily image guidance to ensure that each fraction targets a tumor, instead of healthy tissue. Magnetic resonance imaging (MRI) offers improved soft-tissue visualization, allowing for better tumor and normal tissue delineation. MR-guided RT (MRgRT) has traditionally been defined by the use of offline MRI to aid in defining the RT volumes during the initial planning stages in order to ensure accurate tumor targeting while sparing critical normal tissues. However, the ViewRay MRIdian and Elekta Unity have improved upon and revolutionized the MRgRT by creating a combined MRI and linear accelerator (MRL), allowing MRgRT to incorporate online MRI in RT. MRL-based MR-guided SBRT (MRgSBRT) represents a novel solution to deliver higher doses to larger volumes of gross disease, regardless of the proximity of at-risk organs due to the (1) superior soft-tissue visualization for patient positioning, (2) real-time continuous intrafraction assessment of internal structures, and (3) daily online adaptive replanning. Stereotactic MR-guided adaptive radiation therapy (SMART) has enabled the safe delivery of ablative doses to tumors adjacent to radiosensitive tissues throughout the body. Although it is still a relatively new RT technique, SMART has demonstrated significant opportunities to improve disease control and reduce toxicity. In this review, we included the current clinical applications and the active prospective trials related to SMART. We highlighted the most impactful clinical studies at various tumor sites. In addition, we explored how MRL-based multiparametric MRI could potentially synergize with SMART to significantly change the current treatment paradigm and to improve personalized cancer care.

Methodological considerations on segmenting rhabdomyosarcoma with diffusion-weighted imaging-What can we do better?

PURPOSE

Diffusion-weighted MRI is a promising technique to monitor response to treatment in pediatric rhabdomyosarcoma. However, its validation in clinical practice remains challenging. This study aims to investigate how the tumor segmentation strategy can affect the apparent diffusion coefficient (ADC) measured in pediatric rhabdomyosarcoma.

MATERIALS AND METHODS

A literature review was performed in PubMed using search terms relating to MRI and sarcomas to identify commonly applied segmentation strategies. Seventy-six articles were included, and their presented segmentation methods were evaluated. Commonly reported segmentation strategies were then evaluated on diffusion-weighted imaging of five pediatric rhabdomyosarcoma patients to assess their impact on ADC.

RESULTS

We found that studies applied different segmentation strategies to define the shape of the region of interest (ROI)(outline 60%, circular ROI 27%), to define the segmentation volume (2D 44%, multislice 9%, 3D 21%), and to define the segmentation area (excludes edge 7%, excludes other region 19%, specific area 27%, whole tumor 48%). In addition, details of the segmentation strategy are often unreported. When implementing and comparing these strategies on in-house data, we found that excluding necrotic, cystic, and hemorrhagic areas from segmentations resulted in on average 5.6% lower mean ADC. Additionally, the slice location used in 2D segmentation methods could affect ADC by as much as 66%.

CONCLUSION

Diffusion-weighted MRI studies in pediatric sarcoma currently employ a variety of segmentation methods. Our study shows that different segmentation strategies can result in vastly different ADC measurements, highlighting the importance to further investigate and standardize segmentation.

Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies—a scoping review

Abstract

Musculoskeletal malignancies are a rare type of cancer. Consequently, sufficient imaging data for machine learning (ML) applications is difficult to obtain. The main purpose of this review was to investigate whether ML is already having an impact on imaging-driven diagnosis of musculoskeletal malignancies and what the respective reasons for this might be. A scoping review was conducted by a radiologist, an orthopaedic surgeon and a data scientist to identify suitable articles based on the PRISMA statement. Studies meeting the following criteria were included: primary malignant musculoskeletal tumours, machine/deep learning application, imaging data or data retrieved from images, human/preclinical, English language and original research. Initially, 480 articles were found and 38 met the eligibility criteria. Several continuous and discrete parameters related to publication, patient distribution, tumour specificities, ML methods, data and metrics were extracted from the final articles. For the synthesis, diagnosis-oriented studies were further examined by retrieving the number of patients and labels and metric scores. No significant correlations between metrics and mean number of samples were found. Several studies presented that ML could support imaging-driven diagnosis of musculoskeletal malignancies in distinct cases. However, data quality and quantity must be increased to achieve clinically relevant results. Compared to the experience of an expert radiologist, the studies used small datasets and mostly included only one type of data. Key to critical advancement of ML models for rare diseases such as musculoskeletal malignancies is a systematic, structured data collection and the establishment of (inter)national networks to obtain substantial datasets in the future.

Key Points

• Machine learning does not yet significantly impact imaging-driven diagnosis for musculoskeletal malignancies compared to other disciplines such as lung, breast or CNS cancer.

• Research in the area of musculoskeletal tumour imaging and machine learning is still very limited.

• Machine learning in musculoskeletal tumour imaging is impeded by insufficient availability of data and rarity of the disease.

Imaging update on soft tissue sarcoma

Soft tissue sarcomas (STS) are rare tumours presenting as soft tissue lumps. Ultrasound is often the primary modality for the initial assessment, with MRI the mainstay for lesion characterisation. PET/CT along with other emerging MRI sequences are used in certain situations as an adjunct and problem solving tool in STS staging and assessment of disease recurrence. Recent advances include the promise of whole body MRI, hybrid PET/MRI, diffusion weighted imaging, dynamic contrast enhanced MRI and advances in artificial intelligence. This article discusses current concepts in extremity STS imaging and highlights recent advances.