{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T13:45:28Z","timestamp":1792071928045,"version":"4.4.0"},"reference-count":33,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"vor","delay-in-days":9,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"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":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Transcranial MR\u2010guided focused ultrasound (MRgFUS) enables incisionless thermoablation of deep brain targets and has become an established modality in functional neurosurgery. Treatment efficiency is strongly influenced by skull\u2010mediated ultrasound attenuation. Although the skull density ratio (SDR) is currently the primary parameter used for pre\u2010procedural screening, it provides only a partial description of skull\u2010related variability in thermal response and offers limited guidance for intra\u2010procedural energy titration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To comprehensively characterize skull\u2010related determinants of thermal efficiency beyond SDR and to develop multivariate models for predicting treatment efficiency and sonication\u2010wise peak temperature to support patient screening and intra\u2010procedural decision\u2010making.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We retrospectively analyzed 316 MRgFUS thermoablative procedures (214 thalamotomies and 102 subthalamotomies). Skull metrics derived from CT included SDR, skull thickness (ST), diploe thickness (DT), angle of incidence (AOI), and higher\u2010order distributional descriptors. Thermal efficiency was quantified using multiple temperature\u2010energy\u2010based estimators. Two predictive models were developed, each validated on an independent held\u2010out test set (20% of procedures): (1) a forward\u2010selected multivariate linear regression model for procedure\u2010level thermal efficiency (temperature\u2010to\u2010energy ratio at 55\u00b0C\u2010TER\n                      <jats:sub>55<\/jats:sub>\n                      ), guided by adjusted\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      and AIC, with performance reported as\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      ; and (2) a gradient boosting model for sonication\u2010wise peak temperature prediction, integrating sonication parameters, skull metrics, and cumulative treatment history, with performance reported as MAE globally and stratified by temperature range.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      SDR, ST, and DT were significantly associated with thermal efficiency, and composite metrics such as SDR\/DT outperformed SDR alone. A five\u2010feature multivariable linear regression model based on skull\u2010derived metrics achieved an adjusted\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      of 0.63 (cross\u2010validated\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u00a0=\u00a00.61, MAE\u00a0=\u00a00.54\u00b0C\/kJ) for predicting TER\n                      <jats:sub>55<\/jats:sub>\n                      , with consistent generalization on an independent held\u2010out test set (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u00a0=\u00a00.58, MAE\u00a0=\u00a00.53\u00b0C\/kJ). The peak temperature prediction model achieved a cross\u2010validated MAE of 1.70\u00b0C on the training set and 1.91\u00b0C on a fully held\u2010out procedure\u2010level test set, with stable performance across clinically dominant temperature ranges (&lt;\u00a060\u00b0C). Prediction accuracy was reduced at \u226560\u00b0C, reflecting data imbalance and systematic underestimation at extreme temperatures.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Thermal efficiency in MRgFUS cannot be adequately characterized by SDR alone. A multivariate efficiency model integrating five skull descriptors substantially outperforms SDR\u2010based screening, providing a more informative and continuous representation of treatment feasibility and enabling pre\u2010procedural estimation of expected energy requirements. When combined with treatment parameters and cumulative thermal history, multivariate models enable accurate sonication\u2010wise temperature prediction, supporting efficiency\u2010informed energy titration and dynamic procedural guidance, and advancing MRgFUS toward more precise and personalized therapy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70652","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T07:44:39Z","timestamp":1789026279000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Skull Density Ratio\u2010based eligibility to efficiency\u2010informed planning: Predictive modeling of thermal response in MR\u2010guided focused ultrasound"],"prefix":"10.1002","volume":"53","author":[{"given":"Jos\u00e9 Angel","family":"Pineda\u2010Pardo","sequence":"first","affiliation":[{"name":"HM CINAC (Centro Integral de Neurociencias Abarca Campal) Hospital Universitario HM Puerta del Sur HM Hospitales  M\u00f3stoles Madrid Spain"},{"name":"Instituto de Investigaci\u00f3n Sanitaria HM Hospitales (iHMH)  M\u00f3stoles Madrid Spain"},{"name":"Centro de Investigaci\u00f3n Biom\u00e9dica en Red de Enfermedades Neurodegenerativas (CIBERNED) Instituto de Salud Carlos III  Madrid Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaime","family":"Caballero\u2010Insaurriaga","sequence":"additional","affiliation":[{"name":"HM CINAC (Centro Integral de Neurociencias Abarca Campal) Hospital Universitario HM Puerta del Sur HM Hospitales  M\u00f3stoles Madrid Spain"},{"name":"Instituto de Investigaci\u00f3n Sanitaria HM Hospitales (iHMH)  M\u00f3stoles Madrid Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marta","family":"Castillo\u2010Ortiz","sequence":"additional","affiliation":[{"name":"HM CINAC (Centro Integral de Neurociencias Abarca Campal) Hospital Universitario HM Puerta del Sur HM Hospitales  M\u00f3stoles Madrid Spain"},{"name":"Instituto de Investigaci\u00f3n Sanitaria HM Hospitales (iHMH)  M\u00f3stoles Madrid Spain"},{"name":"PhD Program in Technologies for Health and Well\u2010being Polytechnic University of Valencia  Valencia Spain"},{"name":"Molecular Imaging Technologies Research Institute (I3M) Polytechnic University of Valencia  Valencia Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miguel","family":"L\u00f3pez\u2010Aguirre","sequence":"additional","affiliation":[{"name":"HM CINAC (Centro Integral de Neurociencias Abarca Campal) Hospital Universitario HM Puerta del Sur HM Hospitales  M\u00f3stoles Madrid Spain"},{"name":"Instituto de Investigaci\u00f3n Sanitaria HM Hospitales (iHMH)  M\u00f3stoles Madrid Spain"},{"name":"Centro de Investigaci\u00f3n Biom\u00e9dica en Red de Enfermedades Neurodegenerativas (CIBERNED) Instituto de Salud Carlos III  Madrid Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rafael","family":"Rodr\u00edguez\u2010Rojas","sequence":"additional","affiliation":[{"name":"HM CINAC (Centro Integral de Neurociencias Abarca Campal) Hospital Universitario HM Puerta del Sur HM Hospitales  M\u00f3stoles Madrid Spain"},{"name":"Instituto de Investigaci\u00f3n Sanitaria HM Hospitales (iHMH)  M\u00f3stoles Madrid Spain"},{"name":"Centro de Investigaci\u00f3n Biom\u00e9dica en Red de Enfermedades Neurodegenerativas (CIBERNED) Instituto de Salud Carlos III  Madrid Spain"},{"name":"Centro Universitario HM Hospitales de Ciencias de la Salud (CUHMED) Universidad Camilo Jos\u00e9 Cela  Madrid Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jorge U.","family":"M\u00e1\u00f1ez\u2010Mir\u00f3","sequence":"additional","affiliation":[{"name":"HM CINAC (Centro Integral de Neurociencias Abarca Campal) Hospital Universitario HM Puerta del Sur HM Hospitales  M\u00f3stoles Madrid 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