Blinded assessment of radiological changes after stereotactic ablative radiotherapy (SABR) for early-stage lung cancer: Local recurrences versus fibrosis.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 7520-7520 ◽  
Author(s):  
Suresh Senan ◽  
Kitty Huang ◽  
Sashendra Senthi ◽  
Femke Spoelstra ◽  
Andrew Warner ◽  
...  

7520 Background: Stereotactic ablative radiotherapy (SABR) is a guideline-recommended treatment for unfit patients with early-stage lung cancer. The 5-year local recurrence rates are approximately 10% but fibrotic changes are common during follow-up, leading to difficulty with timely detection and salvage therapies. Previously reported high-risk features (HRFs) on computed tomography (CT) are 1) enlarging opacity at the primary site; 2) sequential enlarging opacity; 3) enlarging opacity after 12 months; 4) bulging margin; 5) loss of linear margin and 6) loss of air bronchograms. We performed a blinded assessment of CT imaging of patients with and without local recurrences. Methods: Patients treated with SABR for early stage lung cancer between 2003 and 2012, who developed pathology-proven local recurrence (n=12), were matched 1:2 to patients without recurrences (n=24), based on baseline factors. The median age at diagnosis was 68 years and median post-SABR imaging follow-up was 24 months (range 6 to 67 months). Patients were well-matched in the recurrence and non-recurrence groups. A total of 153 CT scans were available. Serial CT images were assessed by 3 radiation oncologists blinded to outcomes, viewing anonymized images projected onto a large screen. Results: All established HRFs were significantly associated with local recurrence (p<0.01), and one additional HRF was identified: cranio-caudal growth (p<0.001). The best individual predictor of local recurrence was opacity enlargement after 12 months(100% sensitivity, 83% specificity, p<0.001). The odds of recurrence increased 4-fold for each additional HRF detected in an individual patient. The presence of ≥3 HRFs in an individual patient was highly sensitive and specific for recurrence (both >90%). The HRFs enlarging opacity and cranio-caudal growth were each detected ≥3 months prior to the actual diagnosis of local recurrence in 42% of patients. Conclusions: Local recurrences following SABR can be accurately predicted by the presence of HRF’s on post-treatment CT scans. This approach may reduce unnecessary diagnostic procedures, and ensure earlier use of salvage therapies.

Author(s):  
Guangyao Wu ◽  
Arthur Jochems ◽  
Turkey Refaee ◽  
Abdalla Ibrahim ◽  
Chenggong Yan ◽  
...  

Abstract Introduction Lung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to include (handcrafted and deep) radiomic features. Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes. Methods Here, we outline the latest applications of both structural and functional radiomics in detection, diagnosis, and prediction of pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis in the field of lung cancer. Conclusion The major drawbacks of radiomics are the lack of large datasets with high-quality data, standardization of methodology, the black-box nature of deep learning, and reproducibility. The prerequisite for the clinical implementation of radiomics is that these limitations are addressed. Future directions include a safer and more efficient model-training mode, merge multi-modality images, and combined multi-discipline or multi-omics to form “Medomics.”


2011 ◽  
Vol 6 (12) ◽  
pp. 2036-2043 ◽  
Author(s):  
Cornelis J.A. Haasbeek ◽  
Frank J. Lagerwaard ◽  
Ben J. Slotman ◽  
Suresh Senan

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