{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T17:42:43Z","timestamp":1788370963777,"version":"build-2803163510"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T00:00:00Z","timestamp":1672963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Radiation-induced skin toxicity is a common and distressing side effect of breast radiation therapy (RT). We investigated the use of quantitative spectrophotometric markers as input parameters in supervised machine learning models to develop a predictive model for acute radiation toxicity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and materials<\/jats:title>\n                    <jats:p>\n                      One hundred twenty-nine patients treated for adjuvant whole-breast radiotherapy were evaluated. Two spectrophotometer variables, i.e. the melanin (I\n                      <jats:sub>M<\/jats:sub>\n                      ) and erythema (I\n                      <jats:sub>E<\/jats:sub>\n                      ) indices, were used to quantitatively assess the skin physical changes. Measurements were performed at 4-time intervals: before RT, at the end of RT and 1 and 6 months after the end of RT. Together with clinical covariates, melanin and erythema indices were correlated with skin toxicity, evaluated using the Radiation Therapy Oncology Group (RTOG) guidelines. Binary group classes were labeled according to a RTOG cut-off score of \u2265 2. The patient\u2019s dataset was randomly split into a training and testing set used for model development\/validation and testing (75%\/25% split). A 5-times repeated holdout cross-validation was performed. Three supervised machine learning models, including support vector machine (SVM), classification and regression tree analysis (CART) and logistic regression (LR), were employed for modeling and skin toxicity prediction purposes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Thirty-four (26.4%) patients presented with adverse skin effects (RTOG \u22652) at the end of treatment. The two spectrophotometric variables at the beginning of RT (I\n                      <jats:sub>M,T0<\/jats:sub>\n                      and I\n                      <jats:sub>E,T0<\/jats:sub>\n                      ), together with the volumes of breast (PTV2) and boost surgical cavity (PTV1), the body mass index (BMI) and the dose fractionation scheme (FRAC) were found significantly associated with the RTOG score groups (p&amp;lt;0.05) in univariate analysis. The diagnostic performances measured by the area-under-curve (AUC) were 0.816, 0.734, 0.714, 0.691 and 0.664 for IM, IE, PTV2, PTV1 and BMI, respectively. Classification performances reported precision, recall and F1-values greater than 0.8 for all models. The SVM classifier using the RBF kernel had the best performance, with accuracy, precision, recall and F-score equal to 89.8%, 88.7%, 98.6% and 93.3%, respectively. CART analysis classified patients with I\n                      <jats:sub>M,T0<\/jats:sub>\n                      \u2265 99 to be associated with RTOG \u2265 2 toxicity; subsequently, PTV1 and PTV2 played a significant role in increasing the classification rate. The CART model provided a very high diagnostic performance of AUC=0.959.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Spectrophotometry is an objective and reliable tool able to assess radiation induced skin tissue injury. Using a machine learning approach, we were able to predict grade RTOG \u22652 skin toxicity in patients undergoing breast RT. This approach may prove useful for treatment management aiming to improve patient quality of life.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2022.1044358","type":"journal-article","created":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T12:59:00Z","timestamp":1673009940000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Machine-learning prediction model for acute skin toxicity after breast radiation therapy using spectrophotometry"],"prefix":"10.3389","volume":"12","author":[{"given":"Savino","family":"Cilla","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carmela","family":"Romano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriella","family":"Macchia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mariangela","family":"Boccardi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donato","family":"Pezzulla","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Milly","family":"Buwenge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Augusto Di","family":"Castelnuovo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesca","family":"Bracone","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amalia De","family":"Curtis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chiara","family":"Cerletti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Licia","family":"Iacoviello","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria Benedetta","family":"Donati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Deodato","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessio Giuseppe","family":"Morganti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,1,6]]},"reference":[{"key":"B1","volume-title":"Early and locally advanced breast cancer: Diagnosis and management","year":"2018"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(11)61629-2","article-title":"Effect of radiotherapy after breast-conserving surgery on 10-year recurrence and 15-year breast cancer death: Meta-analysis of individual patient data for 10 801 women in 17 randomised trials","volume":"378","author":"Darby","year":"2011","journal-title":"Lancet"},{"key":"B3","article-title":"Cancer survival in England","volume-title":"Patients diagnosed between 2010 and 2014 and followed up to 2015","year":"2016"},{"key":"B4","volume-title":"Priorities for research on cancer survivorship","year":"2010"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1002\/jso.23804","article-title":"Postmastectomy radiation therapy and immediate autologous breast reconstruction: Integrating perspectives from surgical oncology, radiation oncology, and plastic and reconstructive surgery","volume":"111","author":"Rochlin","year":"2015","journal-title":"J Surg Oncol"},{"key":"B6","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1186\/1471-2407-14-711","article-title":"Factors modifying the risk for developing acute skin toxicity after whole-breast intensity modulated radiotherapy","volume":"14","author":"De Langhe","year":"2014","journal-title":"BMC Cancer"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8140(03)00166-X","article-title":"Personal characteristics, therapy modalities and individual DNA repair capacity as predictive factors of acute skin toxicity in an unselected cohort of breast cancer patients receiving radiotherapy","volume":"69","author":"Twardella","year":"2003","journal-title":"Radiother Oncol"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1016\/j.clon.2011.04.011","article-title":"The Cambridge breast intensity-modulated radiotherapy trial: Patient and treatment-related factors that influence late toxicity","volume":"23","author":"Barnett","year":"2011","journal-title":"Clin Oncol"},{"key":"B9","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1186\/1748-717X-7-217","article-title":"Factors of influence on acute skin toxicity of breast cancer patients treated with standard three-dimensional conformal radiotherapy (3D-CRT) after breast conserving surgery (BCS)","volume":"7","author":"Kraus-Tiefenbacher","year":"2012","journal-title":"Radiat Oncol"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1186\/1748-717X-9-97","article-title":"Toxicity and cosmetic outcome of hypofractionated whole-breast radiotherapy: predictive clinical and dosimetric factors","volume":"9","author":"Ciammella","year":"2014","journal-title":"Radiat Oncol"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2016.03.034","article-title":"Pitfalls in prediction modeling for normal tissue toxicity in radiation therapy: An illustration with the individual radiation sensitivity and mammary carcinoma risk factor investigation cohorts","volume":"95","author":"Mbah","year":"2016","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.00790","article-title":"Machine learning-based models for prediction of toxicity outcomes in radiotherapy","volume":"10","author":"Isaksson","year":"2020","journal-title":"Front Oncol"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2019.12.032","article-title":"Quantitative thermal imaging biomarkers to detect acute skin toxicity from breast radiotherapy using supervised machine learning","volume":"106","author":"Saednia","year":"2020","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"B14","doi-asserted-by":"publisher","first-page":"100890","DOI":"10.1016\/j.adro.2021.100890","article-title":"Development and optimization of a machine-learning prediction model for acute desquamation after breast radiation therapy in the multicenter REQUITE cohort","volume":"7","author":"Aldraimli","year":"2022","journal-title":"Adv Radiat Oncol"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.1017435","article-title":"Prediction of radiation-induced acute skin toxicity in breast cancer patients using data encapsulation screening and dose-gradient-based multi-region radiomics technique: A multicenter study","volume":"12","author":"Feng","year":"2022","journal-title":"Front Oncol"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2022.07.936","article-title":"A machine learning framework for early prediction of radiation dermatitis in patients with breast cancer receiving radiation treatment: A multicenter retrospective analysis study","volume":"114","author":"Li","year":"2022","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1118\/1.3633942","article-title":"Spectrophotometer and ultrasound evaluation of late toxicity following breast-cancer radiotherapy","volume":"38","author":"Yoshida","year":"2011","journal-title":"Med Phys"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2020.02.018","article-title":"Acute radiation-induced skin toxicity in hypofractionated vs. conventional whole-breast irradiation: An objective, randomized multicenter assessment using spectrophotometry","volume":"146","author":"Schmeel","year":"2020","journal-title":"Radiother Oncol"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1016\/j.clnu.2020.09.030","article-title":"Skin toxicity following radiotherapy in patients with breast carcinoma: Is anthocyanin supplementation beneficial","volume":"40","author":"Bracone","year":"2021","journal-title":"Clin Nutr"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1120\/jacmp.v12i2.3451","article-title":"Forward planned intensity modulated radiotherapy (IMRT) for whole breast postoperative radiotherapy. Is it useful? when","volume":"12","author":"Morganti","year":"2011","journal-title":"J Appl Clin Med Phys"},{"key":"B21","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.radonc.2008.10.017","article-title":"Phase I-II studies on accelerated IMRT in breast carcinoma: Technical comparison and acute toxicity in 332 patients","volume":"90","author":"Morganti","year":"2009","journal-title":"Radiother Oncol"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.2147\/BCTT.S261587","article-title":"Intensity-modulated radiotherapy with concomitant boost after breast conserving surgery: A phase I-II trial","volume":"12","author":"Macchia","year":"2020","journal-title":"Breast Cancer"},{"key":"B23","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.ijrobp.2009.04.093","article-title":"Radiation dose-volume effects in the heart","volume":"76","author":"Gagliardi","year":"2010","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.00124","article-title":"Hypofractionated radiotherapy dose scheme and application of new techniques are associated to a lower incidence of radiation pneumonitis in breast cancer patients","volume":"10","author":"Lee","year":"2020","journal-title":"Front Oncol"},{"key":"B25","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1111\/j.0909-752X.2006.00187.x","article-title":"Application of a pigment measuring device-mexameter-for the differential diagnosis of vitiligo and nevus depigmentosus","volume":"2","author":"Park","year":"2006","journal-title":"Skin Res Technol"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1016\/0360-3016(95)00060-C","article-title":"Toxicity criteria of the radiation therapy oncology group (RTOG) and the European organization for research and treatment of cancer (EORTC)","volume":"31","author":"Cox","year":"1995","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"B27","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4612-1694-0_15","article-title":"Information theory and an extension of the maximum likelihood principle","volume-title":"Selected papers of hirotugu akaike. springer series in statistics (Perspectives in statistics)","author":"Akaike","year":"1998"},{"key":"B28","volume-title":"Basic biostatistics for medical and biomedical practitioners (Second edition)","author":"Hoffman","year":"2019"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.738851","article-title":"Risk factors related to acute radiation dermatitis in breast cancer patients after radiotherapy: A systematic review and meta-analysis","volume":"11","author":"Xie","year":"2021","journal-title":"Front Oncol"},{"key":"B30","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1186\/s13014-018-1160-5","article-title":"Acute radiodermatitis in modern adjuvant 3D conformal radiotherapy for breast cancer - the impact of dose distribution and patient related factors","volume":"13","author":"Borm","year":"2018","journal-title":"Radiat Oncol"},{"key":"B31","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.adro.2017.10.007","article-title":"Predictors of radiation-induced acute skin toxicity in breast cancer at a single institution: Role of fractionation and treatment volume","volume":"3","author":"Parekh","year":"2017","journal-title":"Adv Radiat Oncol"},{"key":"B32","doi-asserted-by":"publisher","first-page":"12306","DOI":"10.1038\/s41598-018-30710-4","article-title":"Comparison of radiation dermatitis between hypofractionated and conventionally fractionated postoperative radiotherapy: objective, longitudinal assessment of skin color","volume":"8","author":"Yamazaki","year":"2018","journal-title":"Sci Rep"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000014917","article-title":"Is there agreement between evaluators that used two scoring systems to measure acute radiation dermatitis","volume":"98","author":"Fuzissaki","year":"2019","journal-title":"Medicine"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.1016\/S0360-3016(96)00426-9","article-title":"Prognostic factors for acute and late skin reactions in radiotherapy patients","volume":"36","author":"Turesson","year":"1996","journal-title":"Int J Radiat Oncol"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2014.06.042","article-title":"Racial variations in radiation-induced skin toxicity severity: Data from a prospective cohort receiving postmastectomy radiation","volume":"90","author":"Wright","year":"2014","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2007.04.012","article-title":"Possession of ATM sequence variants as predictor for late normal tissue responses in breast cancer patients treated with radiotherapy","volume":"69","author":"Ho","year":"2007","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"B37","doi-asserted-by":"publisher","DOI":"10.1016\/j.yexcr.2003.11.007","article-title":"Melanin protects melanocytes and keratinocytes against H2O2-induced DNA strand breaks through its ability to bind Ca2+","volume":"294","author":"Hoogduijn","year":"2004","journal-title":"Exp Cell Res"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.1016\/j.jphotobiol.2005.11.008","article-title":"The importance of the depth distribution of melanin in skin for DNA protection and other photobiological processes","volume":"82","author":"Nielsen","year":"2006","journal-title":"J Photochem Photobiol B"},{"key":"B39","doi-asserted-by":"publisher","DOI":"10.1016\/s1470-2045(18)30813-1","article-title":"Hypofractionated versus conventional fractionated postmastectomy radiotherapy for patients with high-risk breast cancer: A randomised, non-inferiority, open-label, phase 3 trial","volume":"20","author":"Wang","year":"2019","journal-title":"Lancet Oncol"},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoncol.2015.2590","article-title":"Differences in the acute toxic effects of breast radiotherapy by fractionation schedule: Comparative analysis of physician-assessed and patient-reported outcomes in a Large multicenter cohort","volume":"1","author":"Jagsi","year":"2015","journal-title":"JAMA Oncol"},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2007.15.2488","article-title":"A multicenter randomized trial of breast intensity-modulated radiation therapy to reduce acute radiation dermatitis","volume":"26","author":"Pignol","year":"2008","journal-title":"J Clin Oncol"},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2016.09.019","article-title":"Evaluation of acute skin toxicity in breast radiotherapy with a new quantitative approach","volume":"122","author":"Gonzalez Sanchis","year":"2017","journal-title":"Radiother Oncol"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2022.1044358\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T12:59:04Z","timestamp":1673009944000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2022.1044358\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,6]]},"references-count":42,"alternative-id":["10.3389\/fonc.2022.1044358"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2022.1044358","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,6]]},"article-number":"1044358"}}