Use of Radiomics to Predict Treatment Response in CNS, Head, Neck and Lung Tumors

PURPOSE/OBJECTIVE(S)

The ability to predict treatment response and local tumor control in cancers treated with radiation might allow for treatment de-escalation in responsive tumors and treatment escalation in non-responsive tumors. The past decade has seen substantial developments in radiomic technology, with some investigators finding radiomic models that predict treatment response and toxicities. While it is standard of care to adapt treatment volumes in the setting of significant tumor shrinkage, it is unclear if deescalating their overall treatment might jeopardize their treatment outcomes. The purpose of this study is to investigate the radiomic features in patients receiving definitive treatment that were re-simulated for significant tumor shrinkage.

MATERIALS/METHODS

We looked for radiomic features correlating with tumor response in 12 female and 16 male patients with a median age of 64 and ECOG status between 0-2. These patients received radiation with curative intent in the CNS, head, neck, or lungs. These patients were re-simulated during the course of their treatment for significant tumor shrinkage to optimize their treatment volumes and minimize radiation dose to organs at risk. Of the 28, 1 patient had CNS cancer, 22 had head or neck cancers, and 15 had lung cancer. Pre- and post- radiation CT planning scans were assessed after the initial gross tumor volume (GTV) was shrunk by 3mm to avoid interface effects of proximal structures. Slope for a straight-line fit was computed between each potential radiomic feature and post-treatment tumor reduction (as calculated using GTV - 3mm) to identify radiomic features that correlate with treatment response. We used the criteria slope >0.5, and slope > 0.6 cut offs to determine the highly predictive radiomic features.

RESULTS

First order mean, which is a surrogate for CT density or the HU value showed a slope of 0 indicating HU as a poor predictor of treatment response. This radiomic feature was deemed stable between patients and between scans. 17 radiomic features showed a slope >0.5 and 5 features demonstrated a slope > 0.6. The 5 most promising included: original_firstorder_minimum, original_glcm_joint average, original_glcm_SumAverage, original_gldm_LargeDependenceHighGrayLevelEmphasis and original_glxzm_LargeAreaHighGrayLevelEmphasis. Of the patients within this group of predictive radiomic features, none had recurrence at the treated disease site.

CONCLUSION

Our study identified 22 radiomic features that might be used to predict treatment response in malignant tumors of the CNS, head, neck and lung. Current tools we use in the clinic, including density and HU values, showed no predictive power for tumor progression but remained stable between patients and between scans. Further research is necessary to determine if these 22 unique radiomic features can be utilized in predicting patient outcomes.

Initial Report of a Randomized Trial Comparing Conventional vs. Novel Treatment Planning Technique to Ameliorate Immunosuppression from Lung SBRT

PURPOSE/OBJECTIVE(S)

SBRT is highly effective against early-stage non-small cell lung cancer. Radiation Therapy (RT) is known to modulate the immune system and contribute to the generation of anti-tumor T cells and stimulate T cell infiltration into tumors. However, this anti-tumor activity is offset by radiation-induced immunosuppression (RIIS) which results in lower tumor control and survival. Lymphocytes are highly radiosensitive and RIIS means destroying existing as well as newly created cytotoxic and helper T lymphocytes. We hypothesized that optimizing RT treatment planning by considering circulating blood and lymphatics as a critical Organ at Risk (OAR) may mitigate RIIS.

MATERIALS/METHODS

We conducted an IRB approved NCI funded clinical trial for 50 early-stage lung cancer patients treated with SBRT alone, from 2020 to 2023, to investigate the ability to reduce RIIS by reducing dose to circulating blood and lymphatics with the aid of a predictive algorithm. All SBRT plans adhered to treatment parameters from RTOG 0813 (central) or RTOG 0915 (peripheral). Patients were randomized to two arms: experimental optimization for RIIS (to reduce dose to blood and lymphatic rich organs) versus standard SBRT planning (without optimization for RIIS). Peripheral blood samples were collected at baseline, end of Tx, 4 weeks and 6 months post Tx. Patients with baseline absolute lymphocyte counts (ALC) less than 0.5x109 cells/L were ineligible for the trial. Data acquired for all blood cell types as well as lymphocyte sub populations CD3+, CD4+, CD8+, CD19+, CD56+. Two sample t-test was used to determine the statistical significance between the cohorts at the time points.

RESULTS

The standard arm had an ALC reduction of 28% at one week post Tx and a nadir at 4 weeks with a 34% reduction. Absolute percentage reductions in ALC from baseline in the optimized arm compared to the standard arm are: end of treatment point (13%, p = 0.03), 4 weeks (12%, p = 0.08), 6 months (15%, p = 0.1), and all three time points together 13% (p = 0.001). ALC recovery appears to be faster in the optimized arm. Radiation induced suppression of all blood cell types are also reduced in the optimized arm with respect to standard arm (relative percentages): ALC (34%), WBC (47%), RBC (46%), platelets (40%), monocytes (100%), and neutrophils (62%) at 4-week mark. Average percentage reductions on integral doses, and V5 (volume receiving a 5 Gy dose) of optimized compared to standard plans are: aorta: 26%, 41% heart: 8%, 33%, vena cava: 32%, 52%, T spine: 51%, 81%, lymph nodes: 35%, 57%, total lung- ITV: 1.6%, 1%, body: 10%, 14%.

CONCLUSION

For the first time, we have shown that it is possible to reduce RIIS in a statistically significant manner, compared to standard of care, via optimized RT planning using a predictive model. This has implications in increasing the efficacy of immunotherapy by preserving the existing tumor reactive T cells in the immune system to enhance anti-tumor activity, and in reducing hospitalizations and improving survival.