Radiomics prognostic analysis of PET/CT images in a multicenter head and neck cancer cohort: investigating ComBat strategies, sub-volume characterization, and automatic segmentation

Radiomics in head and neck squamous cell carcinoma - a leap towards precision oncology

Immunotherapy has revolutionized head and neck squamous cell carcinoma (HNSCC) treatment, with neoadjuvant chemoimmunotherapy showing promising pathological complete response rates (36-42%). Lin et al introduce a radiomics-clinical nomogram using MRI-derived intratumoral and peritumoral features to predict pCR, addressing a critical clinical gap. Their model, emphasizing the peritumoral region (within 3 mm), achieved high predictive accuracy area under curve (AUC) >0.8. While the multicenter design enhances generalizability, standardizing imaging protocols remains a challenge. Integrating radiomics with the Neck Imaging Reporting and Data System could refine post-treatment assessment. This study advances precision oncology in HNSCC, offering a non-invasive tool for personalized treatment strategies. Future directions include artificial intelligence-driven radiomics and radiogenomics to enhance treatment response prediction and patient selection.

Robustness of textural analysis features in quantitative 99 mTc and 177Lu SPECT-CT phantom acquisitions

BACKGROUND

Textural Analysis features in molecular imaging require to be robust under repeat measurement and to be independent of volume for optimum use in clinical studies. Recent EANM and SNMMI guidelines for radiomics provide advice on the potential use of phantoms to identify robust features (Hatt in EJNMMI, 2022). This study applies the suggested phantoms to use in SPECT quantification for two radionuclides, 99 mTc and 177Lu.

METHODS

Acquisitions were made with a uniform phantom to test volume dependency and with a customised 'Revolver' phantom, based on the PET phantom described in Hatt (EJNMMI, 2022) but with local adaptations for SPECT. Each phantom was filled separately with 99 mTc and 177Lu. Sixty-seven Textural Analysis features were extracted and tested for robustness and volume dependency.

RESULTS

Features showing high volume dependency or high Coefficient of Variation (indicating poor repeatability) were removed from the list of features that may be suitable for use in clinical studies. After feature reduction, there were 39 features for 99 mTc and 33 features for 177Lu remaining.

CONCLUSION

The use of a uniform phantom to test volume dependency and a Revolver phantom to identify repeatable Textural Analysis features is possible for quantitative SPECT using 99 mTc or 177Lu. Selection of such features is likely to be centre-dependent due to differences in camera performance as well as acquisition and reconstruction protocols.

Multimodal Imaging Approach for Tumor Treatment Response Evaluation in the Era of Immunotherapy

ABSTRACT

Immunotherapy is likely the most remarkable advancement in lung cancer treatment during the past decade. Although immunotherapy provides substantial benefits, their therapeutic responses differ from those of conventional chemotherapy and targeted therapy, and some patients present unique immunotherapy response patterns that cannot be judged under the current measurement standards. Therefore, the response monitoring of immunotherapy can be challenging, such as the differentiation between real response and pseudo-response. This review outlines the various tumor response patterns to immunotherapy and discusses methods for quantifying computed tomography (CT) and 18 F-fluorodeoxyglucose positron emission tomography (PET) in the field of lung cancer. Emerging technologies in magnetic resonance imaging (MRI) and non-FDG PET tracers are also explored. With immunotherapy responses, the role for imaging is essential in both anatomical radiological responses (CT/MRI) and molecular changes (PET imaging). Multiple aspects must be considered when assessing treatment responses using CT and PET. Finally, we introduce multimodal approaches that integrate imaging and nonimaging data, and we discuss future directions for the assessment and prediction of lung cancer responses to immunotherapy.

Computer Vision-Radiomics & Pathognomics

The role of computer vision in extracting radiographic (radiomics) and histopathologic (pathognomics) features is an extension of molecular biomarkers that have been foundational to our understanding across the spectrum of head and neck disorders. Especially within head and neck cancers, machine learning and deep learning applications have yielded advances in the characterization of tumor features, nodal features, and various outcomes. This review aims to overview the landscape of radiomic and pathognomic applications, informing future work to address gaps. Novel methodologies will be needed to potentially engineer ways of integrating multidimensional data inputs to examine disease features to guide prognosis comprehensively and ultimately clinical management.

Development and Validation of Prognostic Models Using Radiomic Features from Pre-Treatment Positron Emission Tomography (PET) Images in Head and Neck Squamous Cell Carcinoma (HNSCC) Patients

High-dimensional radiomics features derived from pre-treatment positron emission tomography (PET) images offer prognostic insights for patients with head and neck squamous cell carcinoma (HNSCC). Using 124 PET radiomics features and clinical variables (age, sex, stage of cancer, site of cancer) from a cohort of 232 patients, we evaluated four survival models-penalized Cox model, random forest, gradient boosted model and support vector machine-to predict all-cause mortality (ACM), locoregional recurrence/residual disease (LR) and distant metastasis (DM) probability during 36, 24 and 24 months of follow-up, respectively. We developed models with five-fold cross-validation, selected the best-performing model for each outcome based on the concordance index (C-statistic) and the integrated Brier score (IBS) and validated them in an independent cohort of 102 patients. The penalized Cox model demonstrated better performance for ACM (C-statistic = 0.70, IBS = 0.12) and DM (C-statistic = 0.70, IBS = 0.08) while the random forest model displayed better performance for LR (C-statistic = 0.76, IBS = 0.07). We conclude that the ML-based prognostic model can aid clinicians in quantifying prognosis and determining effective treatment strategies, thereby improving favorable outcomes in HNSCC patients.

A novel loss function to reproduce texture features for deep learning-based MRI-to-CT synthesis

BACKGROUND

Studies on computed tomography (CT) synthesis based on magnetic resonance imaging (MRI) have mainly focused on pixel-wise consistency, but the texture features of regions of interest (ROIs) have not received appropriate attention.

PURPOSE

This study aimed to propose a novel loss function to reproduce texture features of ROIs and pixel-wise consistency for deep learning-based MRI-to-CT synthesis. The method was expected to assist the multi-modality studies for radiomics.

METHODS

The study retrospectively enrolled 127 patients with nasopharyngeal carcinoma. CT and MRI images were collected for each patient, and then rigidly registered as pre-procession. We proposed a gray-level co-occurrence matrix (GLCM)-based loss function to improve the reproducibility of texture features. This novel loss function could be embedded into the present deep learning-based framework for image synthesis. In this study, a typical image synthesis model was selected as the baseline, which contained a Unet trained mean square error (MSE) loss function. We embedded the proposed loss function and designed experiments to supervise different ROIs to prove its effectiveness. The concordance correlation coefficient (CCC) of the GLCM feature was employed to evaluate the reproducibility of GLCM features, which are typical texture features. Besides, we used a publicly available dataset of brain tumors to verify our loss function.

RESULTS

Compared with the baseline, the proposed method improved the pixel-wise image quality metrics (MAE: 107.5 to 106.8 HU; SSIM: 0.9728 to 0.9730). CCC values of the GLCM features in GTVnx were significantly improved from 0.78 ± 0.12 to 0.82 ± 0.11 (p < 0.05 for paired t-test). Generally, > 90% (22/24) of the GLCM-based features were improved compared with the baseline, where the Informational Measure of Correlation feature was improved the most (CCC: 0.74 to 0.83). For the public dataset, the loss function also shows its effectiveness. With our proposed loss function added, the ability to reproduce texture features was improved in the ROIs.

CONCLUSIONS

The proposed method reproduced texture features for MRI-to-CT synthesis, which would benefit radiomics studies based on image multi-modality synthesis.

An Innovative and Efficient Diagnostic Prediction Flow for Head and Neck Cancer: A Deep Learning Approach for Multi-Modal Survival Analysis Prediction Based on Text and Multi-Center PET/CT Images

Objective: To comprehensively capture intra-tumor heterogeneity in head and neck cancer (HNC) and maximize the use of valid information collected in the clinical field, we propose a novel multi-modal image-text fusion strategy aimed at improving prognosis. Method: We have developed a tailored diagnostic algorithm for HNC, leveraging a deep learning-based model that integrates both image and clinical text information. For the image fusion part, we used the cross-attention mechanism to fuse the image information between PET and CT, and for the fusion of text and image, we used the Q-former architecture to fuse the text and image information. We also improved the traditional prognostic model by introducing time as a variable in the construction of the model, and finally obtained the corresponding prognostic results. Result: We assessed the efficacy of our methodology through the compilation of a multicenter dataset, achieving commendable outcomes in multicenter validations. Notably, our results for metastasis-free survival (MFS), recurrence-free survival (RFS), overall survival (OS), and progression-free survival (PFS) were as follows: 0.796, 0.626, 0.641, and 0.691. Our results demonstrate a notable superiority over the utilization of CT and PET independently, and exceed the result derived without the clinical textual information. Conclusions: Our model not only validates the effectiveness of multi-modal fusion in aiding diagnosis, but also provides insights for optimizing survival analysis. The study underscores the potential of our approach in enhancing prognosis and contributing to the advancement of personalized medicine in HNC.

Performance of baseline FDG-PET/CT radiomics for prediction of bone marrow minimal residual disease status in the LyMa-101 trial

The prognostic value of 18F-Fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) at baseline or the predictive value of minimal residual disease (MRD) detection appear as potential tools to improve mantle cell lymphoma (MCL) patients' management. The LyMa-101, a phase 2 trial of the LYSA group (ClinicalTrials.gov:NCT02896582) reported induction therapy with obinutuzumab, a CD20 monoclonal antibody. Herein, we investigated the added prognostic value of radiomic features (RF) derived from FDG-PET/CT at diagnosis for MRD value prediction. FDG-PET/CT of 59 MCL patients included in the LyMa-101 trial have been independently, blindly and centrally reviewed. RF were extracted from the disease area with the highest uptake and from the total metabolic tumor volume (TMTV). Two models of machine learning were used to compare several combinations for prediction of MRD before autologous stem cell transplant consolidation (ASCT). Each algorithm was generated with or without constrained feature selections for clinical and laboratory parameters. Both algorithms showed better discrimination performances for negative vs positive MRD in the lesion with the highest uptake than in the TMTV. The constrained use of clinical and biological features showed a clear loss in sensitivity for the prediction of MRD status before ASCT, regardless of the machine learning model. These data plead for the importance of FDG-PET/CT RF compared to clinical and laboratory parameters and also reinforced the previously made hypothesis that the prognosis of the disease in MCL patients is linked to the most aggressive contingent, within the lesion with the highest uptake.