Same-Patient, Same–Time Point Evidence for Radiomics Benchmarking - Summary - MDSpire
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Concurrent Patient Evidence for Benchmarking in Radiomics

  • By

  • Wenze Kan

  • Haoliang Pei

  • Mingxin Zhang

  • October 6, 2026

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Objective:

To evaluate the effectiveness of radiomics-based AI in predicting pathological response following neoadjuvant immunochemotherapy for resectable non-small cell lung cancer.

Approach:
  • Meta-analysis: The study synthesizes data from various AI validation datasets and compares them with conventional criteria for assessing pathological complete response.
Key Findings:
  • AI sensitivity was higher (0.77) compared to conventional methods (0.42), while specificity was lower (0.79 vs 0.97), indicating a trade-off in predictive performance.
  • The study population was limited to patients who underwent radical resection, excluding those who did not reach surgery, which may affect the applicability of the findings.
Interpretation:

The reported results do not demonstrate overall superiority of AI without same-patient data and prespecified costs for false-positive and false-negative decisions.

Limitations:
  • The meta-analysis combines predictions from different treatment stages, which may affect clinical applicability.
  • Patients who failed to undergo surgery due to various reasons were excluded from the analysis, potentially limiting the generalizability of the results.
  • Misinterpretations of RECIST criteria and definitions of pathological response could lead to inaccuracies.
Conclusion:

Future studies should enroll patients at treatment initiation and compare AI with conventional methods while retaining dependence to better assess the impact of radiomics on decision-making.

Original Source(s)

Same-Patient, Same–Time Point Evidence for Radiomics Benchmarking

  • by Wenze Kan, Haoliang Pei, Mingxin Zhang

  • October 6, 2026

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