How does target lesion selection affect RECIST? A computer simulation study
Bucho, T.; Tissier, R.; Lipman, K. G.; Bodalal, Z.; Delli Pizzi, A.; Nguyen-Kim, T. D. L.; Beets-Tan, R.; Trebeschi, S.
Show abstract
RECIST is grounded on the assumption that target lesion selection is objective and representative of the change in total tumor burden during therapy. A computer simulation model was designed to challenge this assumption, focusing on a particular aspect of subjectivity: target lesion selection. Disagreement among readers, and between readers and total tumor burden was analyzed, as a function of the total number of lesions, affected organs, and lesion growth. Disagreement aggravates when the number of lesions increases, when lesions are concentrated on few organs, and when lesion growth borders the thresholds of progressive disease and partial response. An intrinsic methodological error is observed in the estimation of total tumor burden (TTB) via RECIST. In a metastatic setting, RECIST displays a non-linear, unpredictable behavior. Our results demonstrate that RECIST can deliver an accurate estimate of total tumor burden in localized disease, but fails in cases of distal metastases and multiple organ involvement. This is worsened by the "selection of the largest lesions", which introduce a bias that makes it hardly possible to perform an accurate estimate of the total tumor burden. Including more (if not all) lesions in the quantitative analysis of tumor burden is desirable.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Prediction of radiation-induced hypothyroidism using radiomic data analysis does not show superiority over standard normal tissue complication models 91%
- Standardising Breast Radiotherapy Structure Naming Conventions: A Machine Learning Approach 91%
- Dynamic PD-L1 Regulation Shapes Tumor Immune Escape andResponse to Immunotherapy 90%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.