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Cancer Genomics and Diagnostics / Mathematical Biology Tumor Growth · Journal article
Journal of Evolutionary Biology · September 10, 2026
Raises a question worth testing. It does not answer one.
This is a computational modelling study using a virtual NSCLC patient to compare how different imaging measurement strategies (1D, 2D, 3D) detect tumor progression. The authors argue that volumetric (3D) imaging captures tumor burden and dynamics better than RECIST 1.1's one-dimensional approach, and propose that response assessment should evolve to support evolutionary therapy. The work is exploratory and does not provide clinical evidence that this approach improves patient outcomes.
Mathematical modelling study using a virtual patient. Virtual metastatic non-small cell lung cancer patient model.. Intervention: 3D volumetric imaging measurement and analysis.. Compared with: RECIST 1.1 (one-dimensional measurement); two-dimensional imaging..
Lesion selection and measurement dimensionality strongly affect progression detection in virtual model. Two-dimensional metrics provide only modest improvement over RECIST 1.1. Only 3D volumetric measurements accurately capture both tumor burden and disease dynamics.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
This work does not yet provide evidence for clinical practice change. It identifies a methodological hypothesis—that volumetric imaging should replace RECIST 1.1 for evolutionary therapy—that requires prospective clinical validation before adoption in routine care.
A modelling study demonstrating that 3D volumetric imaging outperforms RECIST 1.1 for capturing tumor dynamics in silico; raises a hypothesis about measurement methods rather than testing it clinically.
As stated by the source record.
This work does not yet provide evidence for clinical practice change. It identifies a methodological hypothesis—that volumetric imaging should replace RECIST 1.1 for evolutionary therapy—that requires prospective clinical validation before adoption in routine care.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
What is missing. This record has no reported figures. That is a gap in the analysis, not a judgement about the study.
Evolutionary therapy (ET) aims to anticipate and steer tumor evolution by adjusting treatment timing and dosing, often to control rather than eradicate tumor burden. Clinical use requires reliable monitoring of tumor dynamics to inform mathematical models that guide therapy. In cancers such as metastatic castrate-resistant prostate cancer and relapsed platinum-sensitive ovarian cancer, ET models are informed by serial serum biomarkers. For cancers lacking reliable biomarkers, such as metastatic non-small cell lung cancer (NSCLC), radiographic imaging remains the primary method for treatment response assessment, typically using RECIST 1.1 criteria. RECIST, which tracks a limited number (up to five) of lesions with one-dimensional (1D) measurements and defines progression relative to the nadir, the smallest tumor burden recorded after treatment, was not designed to support ET. It may miss early regrowth, underrepresent tumor burden, and obscure disease trends. Using a virtual NSCLC patient model, we demonstrate that lesion selection and measurement dimensionality strongly affect progression detection. Two-dimensional metrics provide modest improvement, but only 3D volumetric measurements accurately capture both tumor burden and its dynamics, which are key requirements for ET. To support ET in cancers lacking biomarkers, response assessment must evolve beyond RECIST by integrating volumetric imaging, automated segmentation, and potentially liquid biopsies, alongside redefining progression criteria to enable adaptive, patient-centered treatments.
Taken from the source record, never inferred. Follow any of these and new work involving them reaches your briefing.