Eleanor E. R. Harris

Introduction. Positron Emission Tomography/Magnetic Resonance ( PET/MR ) offers benefits over PET/CT including simultaneous PET and MR acquisition, intrinsic spatial registration accuracy, MR-based functional information, and superior soft tissue contrast. However, accurate attenuation correction ( AC ) for PET remains challenging as MR signals do not directly correspond to attenuation. Using deep learning algorithms that learn complex relationships, we generate synthetic CT ( sCT ) from MR for AC. Our novel method for AC, merges deep learning with threshold-based segmentation, to produce an AC map for the entire torso from Dixon MR images, which heretofore has not been demonstrated. Method . Twenty-nine prospectively collected, paired FDG-PET/CT and MR datasets were used for training and validation using the U-net Residual Network conditional Generative Adversarial Network integrated with tissue segmentation ( URcGAN _mod ) from Dixon MR data. Our application focused on torso (base of the skull to mid-thigh) AC, a common but challenging field of view (FOV). Performance was compared to that of 4 previously published methods. Results . Using 15 paired datasets for training and 14 independent datasets for testing, the URcGAN _mod generates an accurate torso sCT with a mean absolute difference of 32 ± 4 HU per voxel. When applied for AC for FDG images, and considering evaluable (SUV ≥ 0.1 g/ml) voxels across all regions of interest, absolute values of the differences were within 4.4% from those determined using the measured CT for AC. Reproducibility was excellent with less than 3.5% standard deviation. The results demonstrate the accuracy and precision of URcGAN _mod method for torso sCT generation for quantitatively accurate MR-based AC ( MRAC ), exceeding the comparison methods. Conclusion . Combining deep learning and segmentation enhances MRAC accuracy in torso FDG-PET/MR, improves SUV accuracy throughout the torso, achieves less than 4.4% SUV error, and outperforms comparison methods. Given the excellent sCT and SUV accuracy and precision, our proposed method warrants further studies for quantitative longitudinal multicenter trials.

PURPOSE: Multiple studies have shown a low risk of ipsilateral breast events (IBEs) or other recurrences for selected patients age 65-70 years or older with stage I breast cancers treated with breast-conserving surgery (BCS) and endocrine therapy (ET) without adjuvant radiotherapy. We sought to evaluate whether younger postmenopausal patients could also be successfully treated without radiation therapy, adding a genomic assay to classic selection factors. METHODS: Postmenopausal patients age 50-69 years with pT1N0 unifocal invasive breast cancer with margins ≥2 mm after BCS whose tumors were estrogen receptor–positive, progesterone receptor–positive, and human epidermal growth factor receptor 2–negative with Oncotype DX 21-gene recurrence score ≤18 were prospectively enrolled in a single-arm trial of radiotherapy omission if they consented to take at least 5 years of ET. The primary end point was the rate of locoregional recurrence 5 years after BCS. RESULTS: Between June 2015 and October 2018, 200 eligible patients were enrolled. Among the 186 patients with clinical follow-up of at least 56 months, overall and breast cancer–specific survival rates at 5 years were both 100%. The 5-year freedom from any recurrence was 99% (95% CI, 96 to 100). Crude rates of IBEs for the entire follow-up period for patients age 50-59 years and age 60-69 years were 3.3% (2/60) and 3.6% (5/140), respectively; crude rates of overall recurrence were 5.0% (3/60) and 3.6% (5/140), respectively. CONCLUSION: This trial achieved a very low risk of recurrence using a genomic assay in combination with classic clinical and biologic features for treatment selection, including postmenopausal patients younger than 60 years. Long-term follow-up of this trial and others will help determine whether the option of avoiding initial radiotherapy can be offered to a broader group of women than current guidelines recommend.

PDF file, 81K, Online Table 1. Clinical Characteristics of Patients in Both Datasets. Online Table 2. Distribution of Recurrences by Group (Karolinka Dataset).

PDF file, 81K, Online Table 1. Clinical Characteristics of Patients in Both Datasets. Online Table 2. Distribution of Recurrences by Group (Karolinka Dataset).

Purpose: Previously, we developed a radiosensitivity molecular signature [radiosensitivity index (RSI)] that was clinically validated in 3 independent datasets (rectal, esophageal, and head and neck) in 118 patients. Here, we test RSI in radiotherapy (RT)-treated breast cancer patients.Experimental Design: RSI was tested in 2 previously published breast cancer datasets. Patients were treated at the Karolinska University Hospital (n = 159) and Erasmus Medical Center (n = 344). RSI was applied as previously described.Results: We tested RSI in RT-treated patients (Karolinska). Patients predicted to be radiosensitive (RS) had an improved 5-year relapse-free survival when compared with radioresistant (RR) patients (95% vs. 75%, P = 0.0212), but there was no difference between RS/RR patients treated without RT (71% vs. 77%, P = 0.6744), consistent with RSI being RT-specific (interaction term RSI × RT, P = 0.05). Similarly, in the Erasmus dataset, RT-treated RS patients had an improved 5-year distant metastasis-free survival over RR patients (77% vs. 64%, P = 0.0409), but no difference was observed in patients treated without RT (RS vs. RR, 80% vs. 81%, P = 0.9425). Multivariable analysis showed RSI is the strongest variable in RT-treated patients (Karolinska, HR = 5.53, P = 0.0987, Erasmus, HR = 1.64, P = 0.0758) and in backward selection (removal α of 0.10), RSI was the only variable remaining in the final model. Finally, RSI is an independent predictor of outcome in RT-treated ER+ patients (Erasmus, multivariable analysis, HR = 2.64, P = 0.0085).Conclusions: RSI is validated in 2 independent breast cancer datasets totaling 503 patients. Including prior data, RSI is validated in 5 independent cohorts (621 patients) and represents, to our knowledge, the most extensively validated molecular signature in radiation oncology. Clin Cancer Res; 18(18); 5134–43. ©2012 AACR.