Radiomics in radiotherapy: Applications and future challenges

Machine learning-based analysis of 68Ga-PSMA-11 PET/CT images for estimation of prostate tumor grade

Early diagnosis of prostate cancer, the most common malignancy in men, can improve patient outcomes. Since the tissue sampling procedures are invasive and sometimes inconclusive, an alternative image-based method can prevent possible complications and facilitate treatment management. We aim to propose a machine-learning model for tumor grade estimation based on 68 Ga-PSMA-11 PET/CT images in prostate cancer patients. This study included 90 eligible participants out of 244 biopsy-proven prostate cancer patients who underwent staging 68Ga-PSMA-11 PET/CT imaging. The patients were divided into high and low-intermediate groups based on their Gleason scores. The PET-only images were manually segmented, both lesion-based and whole prostate, by two experienced nuclear medicine physicians. Four feature selection algorithms and five classifiers were applied to Combat-harmonized and non-harmonized datasets. To evaluate the model's generalizability across different institutions, we performed leave-one-center-out cross-validation (LOOCV). The metrics derived from the receiver operating characteristic curve were used to assess model performance. In the whole prostate segmentation, combining the ANOVA algorithm as the feature selector with Random Forest (RF) and Extra Trees (ET) classifiers resulted in the highest performance among the models, with an AUC of 0.78 and 083, respectively. In the lesion-based segmentation, the highest AUC was achieved by MRMR feature selector + Linear Discriminant Analysis (LDA) and Logistic Regression (LR) classifiers (0.76 and 0.79, respectively). The LOOCV results revealed that both the RF_ANOVA and ET_ANOVA models showed high levels of accuracy and generalizability across different centers, with an average AUC value of 0.87 for the ET_ANOVA combination. Machine learning-based analysis of radiomics features extracted from 68Ga-PSMA-11 PET/CT scans can accurately classify prostate tumors into low-risk and intermediate- to high-risk groups.

Quantitation of Oncologic Image Features for Radiomic Analyses in PET

Radiomics is an emerging and exciting field of study involving the extraction of many quantitative features from radiographic images. Positron emission tomography (PET) images are used in cancer diagnosis and staging. Utilizing radiomics on PET images can better quantify the spatial relationships between image voxels and generate more consistent and accurate results for diagnosis, prognosis, treatment, etc. This chapter gives the general steps a researcher would take to extract PET radiomic features from medical images and properly develop models to implement.

What We Talk about When We Talk about Artificial Intelligence in Radiation Oncology

The constant evolution of technology has dramatically changed the history of radiation oncology, allowing clinicians to deliver increasingly accurate and precise treatments, moving from 2D radiotherapy to 3D conformal radiotherapy, leading to intensity-modulated image-guided (IMRT-IGRT) and stereotactic body radiotherapy treatments [...].

Challenges in Glioblastoma Radiomics and the Path to Clinical Implementation

Radiomics is a field of medical imaging analysis that focuses on the extraction of many quantitative imaging features related to shape, intensity and texture. These features are incorporated into models designed to predict important clinical or biological endpoints for patients. Attention for radiomics research has recently grown dramatically due to the increased use of imaging and the availability of large, publicly available imaging datasets. Glioblastoma multiforme (GBM) patients stand to benefit from this emerging research field as radiomics has the potential to assess the biological heterogeneity of the tumour, which contributes significantly to the inefficacy of current standard of care therapy. Radiomics models still require further development before they are implemented clinically in GBM patient management. Challenges relating to the standardisation of the radiomics process and the validation of radiomic models impede the progress of research towards clinical implementation. In this manuscript, we review the current state of radiomics in GBM, and we highlight the barriers to clinical implementation and discuss future validation studies needed to advance radiomics models towards clinical application.

Novel Imaging Biomarkers to Assess Oncologic Treatment-Related Changes

Cancer therapeutics cause various treatment-related changes that may impact patient follow-up and disease monitoring. Although atypical responses such as pseudoprogression may be misinterpreted as treatment nonresponse, other changes, such as hyperprogressive disease seen with immunotherapy, must be recognized early for timely management. Radiation necrosis in the brain is a known response to radiotherapy and must be distinguished from local tumor recurrence. Radiotherapy can also cause adverse effects such as pneumonitis and local tissue toxicity. Systemic therapies, like chemotherapy and targeted therapies, are known to cause long-term cardiovascular effects. Thus, there is a need for robust biomarkers to identify, distinguish, and predict cancer treatment-related changes. Radiomics, which refers to the high-throughput extraction of subvisual features from radiologic images, has been widely explored for disease classification, risk stratification, and treatment-response prediction. Lately, there has been much interest in investigating the role of radiomics to assess oncologic treatment-related changes. We review the utility and various applications of radiomics in identifying and distinguishing atypical responses to treatments, as well as in predicting adverse effects. Although artificial intelligence tools show promise, several challenges-including multi-institutional clinical validation, deployment in health care settings, and artificial-intelligence bias-must be addressed for seamless clinical translation of these tools.

Translation of Precision Medicine Research Into Biomarker-Informed Care in Radiation Oncology

The reach of personalized medicine in radiation oncology has expanded greatly over the past few decades as technical precision has improved the delivery of radiation to each patient's unique anatomy. Yet, the consideration of biological heterogeneity between patients has largely not been translated to clinical care. There are innumerable promising advancements in the discovery and validation of biomarkers, which could be used to alter radiation therapy directly or indirectly. Directly, biomarker-informed care may alter treatment dose or identify patients who would benefit most from radiation therapy and who could safely avoid more aggressive care. Indirectly, a variety of biomarkers could assist with choosing the best radiosensitizing chemotherapies. The translation of these advancements into clinical practice will bring radiation oncology even further into the era of precision medicine, treating patients according to their unique anatomical and biological differences.

Radiomics in surgical oncology: applications and challenges

Surgery is a curative treatment option for many patients with malignant tumors. Increased attention has focused on the combination of surgery with chemotherapy, as multimodality treatment has been associated with promising results in certain cancer types. Despite these data, there remains clinical equipoise on optimal timing and patient selection for neoadjuvant or adjuvant strategies. Radiomics, an emerging field involving the extraction of advanced features from radiographic images, has the potential to revolutionize oncologic treatment and contribute to the advance of personalized therapy by helping predict tumor behavior and response to therapy. This review analyzes and summarizes studies that use radiomics with machine learning in patients who have received neoadjuvant and/or adjuvant chemotherapy to predict prognosis, recurrence, survival, and therapeutic response for various cancer types. While studies in both neoadjuvant and adjuvant settings demonstrate above average performance on ability to predict progression-free and overall survival, there remain many challenges and limitations to widespread implementation of this technology. The lack of standardization of common practices to analyze radiomics, limited data sharing, and absence of auto-segmentation have hindered the inclusion and rapid adoption of radiomics in prospective, clinical studies.

Development and validation of radiomics model built by incorporating machine learning for identifying liver fibrosis and early-stage cirrhosis

BACKGROUND

Liver fibrosis (LF) continues to develop and eventually progresses to cirrhosis. However, LF and early-stage cirrhosis (ESC) can be reversed in some cases, while advanced cirrhosis is almost impossible to cure. Advances in quantitative imaging techniques have made it possible to replace the gold standard biopsy method with non-invasive imaging, such as radiomics. Therefore, the purpose of this study is to develop a radiomics model to identify LF and ESC.

METHODS

Patients with LF (n = 108) and ESC (n = 116) were enrolled in this study. As a control, patients with healthy livers were involved in the study (n = 145). Diffusion-weighted imaging (DWI) data sets with three b-values (0, 400, and 800 s/mm) of enrolled cases were collected in this study. Then, radiomics features were extracted from manually delineated volumes of interest. Two modeling strategies were performed after univariate analysis and feature selection. Finally, an optimal model was determined by the receiver operating characteristic area under the curve (AUC).

RESULTS

The optimal models were built in plan 1. For model 1 in plan 1, the AUCs of the training and validation cohorts were 0.973 (95% confidence interval [CI] 0.946-1.000) and 0.948 (95% CI 0.903-0.993), respectively. For model 2 in plan 1, the AUCs of the training and validation cohorts were 0.944, 95% CI 0.905 to 0.983, and 0.968, 95% CI 0.940 to 0.996, respectively.

CONCLUSIONS

Radiomics analysis of DWI images allows for accurate identification of LF and ESC, and the non-invasive biomarkers extracted from the functional DWI images can serve as a better alternative to biopsy.