{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T14:37:24Z","timestamp":1793284644064,"version":"4.5.0"},"reference-count":51,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Tumor control probability (TCP) modeling for early\u2010stage non\u2010small cell lung cancer (NSCLC) is usually performed by one\u2010dimensional (1D) fitting that assumes error\u2010free dose and assigns all uncertainty to the local control (LC) axis. Neglecting dose\u2010axis uncertainties leads to a classical errors\u2010in\u2010variables (EIV) problem that can bias both the dose required for 50% TCP () and curve steepness. Furthermore, the mechanistic origins of TCP steepness remain incompletely characterized, limiting the translational link between cellular radiobiology and clinical\u00a0dose\u2013response.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop a mechanistic TCP framework that (i) mitigates EIV bias by explicitly modeling uncertainties in both dose and LC, (ii) decomposes TCP steepness into contributions from clonogenic heterogeneity and inter\u2010patient radiosensitivity, and (iii) validates the translational\u00a0consistency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Cell\u2010survival data from A549 NSCLC cells were fitted with three radiobiological models\u2014saturable repair and repairability (SRR), universal survival curve (USC), and linear\u2013quadratic (LQ)\u2014using maximum\u2010likelihood estimation. Clinical TCP modeling used 34 early\u2010stage NSCLC studies (41 data points, 3\u2010year LC). Prescriptions were converted to EQD2 using each model's formalism, and TCP curves were fitted by a two\u2010dimensional (2D) maximum\u2010likelihood framework. The LC\u2010axis uncertainty was constructed from binomial sampling uncertainty and an intrinsic scatter, the latter co\u2010estimated with the effective dose\u2010axis uncertainty. TCP steepness was measured by the inter\u2010patient variability  of the TCP exponent and decomposed into clonogenic heterogeneity (, from reported tumor volumes) and inter\u2010patient radiosensitivity variability ().<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In cell\u2010survival fitting, the SRR and USC models accurately reproduced A549 survival (AIC =  and 0.21); the LQ model achieved the lowest AIC () but only by inflating  to 31.1\u00a0Gy. In clinical fitting, the SRR, USC, and LQ models yielded  = 57.1, 51.6, and 50.2\u00a0Gy EQD2 and  = 81.4, 71.8, and 60.5\u00a0Gy. The effective dose\u2010axis uncertainty (16.6%, 16.9%, and 10.5%) exceeded the mean relative LC\u2010axis uncertainty (5.0%, 5.0%, and 5.8%), supporting the need for a 2D framework. Variance decomposition revealed that clonogenic heterogeneity contributed only 3%\u201315% of , while inter\u2010patient radiosensitivity dominated (85%\u201397%);  = 0.057\u20130.120 remained within the CLARIFi\u2010derived inter\u2010cell\u2010line  bound of 0.255 (across 41 NSCLC cell lines). Fitted  for the SRR and USC models implied mean tumor diameters of 2.6 and 3.2\u00a0cm, both falling within approximately one standard deviation of the clinical reference \u00a0cm; the LQ\u2010model substantially overestimated the diameter (6.0\u00a0cm).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed 2D maximum\u2010likelihood framework mitigates EIV bias and links clinical TCP to mechanistic cell\u2010survival models. In early\u2010stage NSCLC, inter\u2010patient radiosensitivity dominates TCP steepness while clonogenic cell\u2010number variability contributes negligibly. This framework provides a robust basis for future TCP modeling and dose\u2010prescription\u00a0studies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70619","type":"journal-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T11:11:45Z","timestamp":1785496305000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Translational tumor control probability modeling for NSCLC: A two\u2010dimensional maximum\u2010likelihood framework"],"prefix":"10.1002","volume":"53","author":[{"given":"Ryoichi","family":"Hinoto","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Saitama Red Cross Hospital Saitama Saitama Japan"},{"name":"Institute of Medicine University of Tsukuba Tsukuba Ibaraki Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takeji","family":"Sakae","sequence":"additional","affiliation":[{"name":"Institute of Medicine University of Tsukuba 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