Radiotherapy dose painting by circadian rhythm based radiomics

Clinical analysis of the efficacy of radiation therapy for primary high-grade gliomas guided by biological rhythms

OBJECTIVE

High-grade glioma (HGG) patients frequently encounter treatment resistance and relapse, despite numerous interventions seeking enhanced survival outcomes yielding limited success. Consequently, this study, rooted in our prior research, aimed to ascertain whether leveraging circadian rhythm phase attributes could optimize radiotherapy results.

METHODS

In this retrospective analysis, we meticulously selected 121 HGG cases with synchronized rhythms through Cosinor analysis. Post-surgery, all subjects underwent standard radiotherapy alongside Temozolomide chemotherapy. Random allocation ensued, dividing patients into morning (N = 69) and afternoon (N = 52) radiotherapy cohorts, enabling a comparison of survival and toxicity disparities.

RESULTS

The afternoon radiotherapy group exhibited improved overall survival (OS) and progression-free survival (PFS) relative to the morning cohort. Notably, median OS extended to 25.6 months versus 18.5 months, with P = 0.014, with median PFS at 20.6 months versus 13.3 months, with P = 0.022, post-standardized radiotherapy. Additionally, lymphocyte expression levels in the afternoon radiation group 32.90(26.10, 39.10) significantly exceeded those in the morning group 31.30(26.50, 39.20), with P = 0.032.

CONCLUSIONS

This study underscores the markedly prolonged average survival within the afternoon radiotherapy group. Moreover, lymphocyte proportion demonstrated a notable elevation in the afternoon group. Timely and strategic adjustments of therapeutic interventions show the potential to improve therapeutic efficacy, while maintaining vigilant systemic immune surveillance. A comprehensive grasp of physiological rhythms governing both the human body and tumor microenvironment can refine treatment efficacy, concurrently curtailing immune-related damage-a crucial facet of precision medicine.

Potential advantages of FDG-PET radiomic feature map for target volume delineation in lung cancer radiotherapy

PURPOSE

To investigate the potential benefits of FDG PET radiomic feature maps (RFMs) for target delineation in non-small cell lung cancer (NSCLC) radiotherapy.

METHODS

Thirty-two NSCLC patients undergoing FDG PET/CT imaging were included. For each patient, nine grey-level co-occurrence matrix (GLCM) RFMs were generated. gross target volume (GTV) and clinical target volume (CTV) were contoured on CT (GTV_CT , CTV_CT ), PET (GTV_PET40 , CTV_PET40 ), and RFMs (GTV_RFM , CTV_RFM ,). Intratumoral heterogeneity areas were segmented as GTV_PET50-Boost and radiomic boost target volume (RTV_Boost ) on PET and RFMs, respectively. GTV_CT in homogenous tumors and GTV_PET40 in heterogeneous tumors were considered as GTV_{gold standard} (GTV_GS ). One-way analysis of variance was conducted to determine the threshold that finds the best conformity for GTV_RFM with GTV_GS . Dice similarity coefficient (DSC) and mean absolute percent error (MAPE) were calculated. Linear regression analysis was employed to report the correlations between the gold standard and RFM-derived target volumes.

RESULTS

Entropy, contrast, and Haralick correlation (H-correlation) were selected for tumor segmentation. The threshold values of 80%, 50%, and 10% have the best conformity of GTV_RFM-entropy , GTV_RFM-contrast , and GTV_{RFM-H-correlation} with GTV_GS , respectively. The linear regression results showed a positive correlation between GTV_GS and GTV_RFM-entropy (r = 0.98, p < 0.001), between GTV_GS and GTV_RFM-contrast (r = 0.93, p < 0.001), and between GTV_GS and GTV_{RFM-H-correlation} (r = 0.91, p < 0.001). The average threshold values of 45% and 15% were resulted in the best segmentation matching between CTV_RFM-entropy and CTV_RFM-contrast with CTV_GS , respectively. Moreover, we used RFM to determine RTV_Boost in the heterogeneous tumors. Comparison of RTV_Boost with GTV_PET50-Boost MAPE showed the volume error differences of 31.7%, 36%, and 34.7% in RTV_{Boost-entropy} , RTV_{Boost-contrast} , and RTV_{Boost-H-correlation} , respectively.

CONCLUSIONS

FDG PET-based radiomics features in NSCLC demonstrated a promising potential for decision support in radiotherapy, helping radiation oncologists delineate tumors and generate accurate segmentation for heterogeneous region of tumors.

Chronoradiobiology of Breast Cancer: The Time Is Now to Link Circadian Rhythm and Radiation Biology

Circadian disruption has been linked to cancer development, progression, and radiation response. Clinical evidence to date shows that circadian genetic variation and time of treatment affect radiation response and toxicity for women with breast cancer. At the molecular level, there is interplay between circadian clock regulators such as PER1, which mediates ATM and p53-mediated cell cycle gating and apoptosis. These molecular alterations may govern aggressive cancer phenotypes, outcomes, and radiation response. Exploiting the various circadian clock mechanisms may enhance the therapeutic index of radiation by decreasing toxicity, increasing disease control, and improving outcomes. We will review the body’s natural circadian rhythms and clock gene-regulation while exploring preclinical and clinical evidence that implicates chronobiological disruptions in the etiology of breast cancer. We will discuss radiobiological principles and the circadian regulation of DNA damage responses. Lastly, we will present potential rational therapeutic approaches that target circadian pathways to improve outcomes in breast cancer. Understanding the implications of optimal timing in cancer treatment and exploring ways to entrain circadian biology with light, diet, and chronobiological agents like melatonin may provide an avenue for enhancing the therapeutic index of radiotherapy.

Radiographic Texture Reproducibility: The Impact of Different Materials, their Arrangement, and Focal Spot Size

Background:

Feature reproducibility is a critical issue in quantitative radiomic studies. The aim of this study is to assess how radiographic radiomic textures behave against changes in phantom materials, their arrangements, and focal spot size.

Method:

A phantom with detachable parts was made using wood, sponge, Plexiglas, and rubber. Each material had 1 cm thickness and was imaged for consecutive time. The phantom also was imaged by change in the arrangement of its materials. Imaging was done with two focal spot sizes including 0.6 and 1.2 mm. All images were acquired with a digital radiography machine. Several texture features were extracted from the same size region of interest in all images. To assess reproducibility, coefficient of variation (COV), intraclass correlation coefficient (ICC), and Bland–Altman tests were used.

Results:

Results show that 59%, 50%, and 4.5% of all features are most reproducible (COV ≤5%) against change in focal spot size, material arrangements, and phantom's materials, respectively. Results on Bland–Altman analysis showed that there is just a nonreproducible feature against change in the focal spot size. On the ICC results, we observed that the ICCs for more features are >0.90 and there were few features with ICC lower than 0.90.

Conclusion:

We showed that radiomic textures are vulnerable against changes in materials, arrangement, and different focal spot sizes. These results suggest that a careful analysis of the effects of these parameters is essential before any radiomic clinical application.

Radiographic Image Radiomics Feature Reproducibility: A Preliminary Study on the Impact of Field Size

RATIONALE AND OBJECTIVES

Radiomics is an approach to quantifying diseases. Recently, several studies have indicated that radiomics features are vulnerable against imaging parameters. The aim of this study is to assess how radiomics features change with radiographic field sizes, positions in the field size, and mAs.

MATERIALS AND METHODS

A large and small wood phantom and a cotton phantom were prepared and imaged in different field sizes, mAs, and placement in the radiographic field size. A region of interest was drawn on the image features, and twenty two features were extracted. Radiomics feature reproducibility was obtained based on coefficient of variation, Bland-Altman analysis, and intraclass correlation coefficient. Features with coefficient of variation ≤ 5%, intraclass correlation coefficient ≤ 90%, and 1% ≤ U/LRL ≤30% were introduced as robust features. U/LRL is upper/lower reproducibility limits in Bland-Altman.

RESULTS

For all field sizes and all phantoms, features including Difference Variance, Inverse Different Moment, Fraction, Long Run Emphasis, Run Length Non Uniformity, and Short Run Emphasis were found as highly reproducible features. For change in the position of field size, Fraction was the most reproducible in all field sizes and all phantoms. On the mAs change, we found that feature, Short Run Emphasis field 15 × 15 for small wood phantom, and Correlation in all field sizes for Cotton are the most reproducible features.

CONCLUSION

We demonstrated that radiomics features are strongly vulnerable against radiographic field size, positions in the radiation field, mAs, and phantom materials, and reproducibility analyses should be performed before each radiomics study. Moreover, these changing parameters should be considered, and their effects should be minimized in future radiomics studies.

Radiomics for classification of bone mineral loss: A machine learning study

PURPOSE

The purpose of this study was to develop predictive models to classify osteoporosis, osteopenia and normal patients using radiomics and machine learning approaches.

MATERIALS AND METHODS

A total of 147 patients were included in this retrospective single-center study. There were 12 men and 135 women with a mean age of 56.88±10.6 (SD) years (range: 28-87 years). For each patient, seven regions including four lumbar and three femoral including trochanteric, intertrochanteric and neck were segmented on bone mineral densitometry images and 54 texture features were extracted from the regions. The performance of four feature selection methods, including classifier attribute evaluation (CLAE), one rule attribute evaluation (ORAE), gain ratio attribute evaluation (GRAE) and principal components analysis (PRCA) along with four classification methods, including random forest (RF), random committee (RC), K-nearest neighbor (KN) and logit-boost (LB) were evaluated. Four classification categories, including osteopenia vs. normal, osteoporosis vs. normal, osteopenia vs. osteoporosis and osteoporosis+osteopenia vs. osteoporosis were examined for the defined seven regions. The classification model performances were evaluated using the area under the receiver operator characteristic curve (AUC).

RESULTS

The AUC values ranged from 0.50 to 0.78. The combination of methods RF+CLAE, RF+ORAE and RC+ORAE yielded highest performance (AUC=0.78) in discriminating between osteoporosis and normal state in the trochanteric region. The combinations of RF+PRCA and LB+PRCA had the highest performance (AUC=0.76) in discriminating between osteoporosis and normal state in the neck region.

CONCLUSION

The machine learning radiomic approach can be considered as a new method for bone mineral deficiency disease classification using bone mineral densitometry image features.