Machine-learning prediction model for acute skin toxicity after breast radiation therapy using spectrophotometry

PurposeRadiation-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. Methods and materialsOne hundred twenty-nine patients treated for adjuvant whole-breast radiotherapy were evaluated. Two spectrophotometer variables, i.e. the melanin (I-M) and erythema (I-E) 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 >= 2. The patient's 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. ResultsThirty-four (26.4%) patients presented with adverse skin effects (RTOG >= 2) at the end of treatment. The two spectrophotometric variables at the beginning of RT (I-M,I-T0 and I-E,I-T0), 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<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-M,I-T0 >= 99 to be associated with RTOG >= 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. ConclusionsSpectrophotometry 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 >= 2 skin toxicity in patients undergoing breast RT. This approach may prove useful for treatment management aiming to improve patient quality of life

Identifying brain tumor patients’ subtypes based on pre-diagnostic history and clinical characteristics: a pilot hierarchical clustering and association analysis

IntroductionCentral nervous system (CNS) tumors are severe health conditions with increasing incidence in the last years. Different biological, environmental and clinical factors are thought to have an important role in their epidemiology, which however remains unclear.ObjectiveThe aim of this pilot study was to identify CNS tumor patients’ subtypes based on this information and to test associations with tumor malignancy.Methods90 patients with suspected diagnosis of CNS tumor were recruited by the Neurosurgery Unit of IRCCS Neuromed. Patients underwent anamnestic and clinical assessment, to ascertain known or suspected risk factors including lifestyle, socioeconomic, clinical and psychometric characteristics. We applied a hierarchical clustering analysis to these exposures to identify potential groups of patients with a similar risk pattern and tested whether these clusters associated with brain tumor malignancy.ResultsOut of 67 patients with a confirmed CNS tumor diagnosis, we identified 28 non-malignant and 39 malignant tumor cases. These subtypes showed significant differences in terms of gender (with men more frequently presenting a diagnosis of cancer; p = 6.0 ×10−3) and yearly household income (with non-malignant tumor patients more frequently earning ≥25k Euros/year; p = 3.4×10−3). Cluster analysis revealed the presence of two clusters of patients: one (N=41) with more professionally active, educated, wealthier and healthier patients, and the other one with mostly retired and less healthy men, with a higher frequency of smokers, personal history of cardiovascular disease and cancer familiarity, a mostly sedentary lifestyle and generally lower income, education and cognitive performance. The former cluster showed a protective association with the malignancy of the disease, with a 74 (14-93) % reduction in the prevalent risk of CNS malignant tumors, compared to the other cluster (p=0.026).DiscussionThese preliminary data suggest that patients’ profiling through unsupervised machine learning approaches may somehow help predicting the risk of being affected by a malignant form. If confirmed by further analyses in larger independent cohorts, these findings may be useful to create potential intelligent ranking systems for treatment priority, overcoming the lack of histopathological information and molecular diagnosis of the tumor, which are typically not available until the time of surgery

Machine-learning prediction model for acute skin toxicity after breast radiation therapy using spectrophotometry

PurposeRadiation-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.Methods and materialsOne hundred twenty-nine patients treated for adjuvant whole-breast radiotherapy were evaluated. Two spectrophotometer variables, i.e. the melanin (IM) and erythema (IE) 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 ≥ 2. The patient’s 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.ResultsThirty-four (26.4%) patients presented with adverse skin effects (RTOG ≥2) at the end of treatment. The two spectrophotometric variables at the beginning of RT (IM,T0 and IE,T0), 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<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 IM,T0 ≥ 99 to be associated with RTOG ≥ 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.ConclusionsSpectrophotometry 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 ≥2 skin toxicity in patients undergoing breast RT. This approach may prove useful for treatment management aiming to improve patient quality of life

Psychological Resilience, Cardiovascular Disease, and Metabolic Disturbances: A Systematic Review

Background: Positive psychosocial factors can play an important role in the development of cardiovascular disease (CVD). Among them, psychological resilience (PR) is defined as the capacity of responding positively to stressful events. Our aim was to assess whether PR is associated with CVD or metabolic disturbances through a systematic review. Methods: We gathered articles from PubMed, Web of Science, PsycInfo, and Google Scholar up to October 28, 2021. We included articles that were in English, were observational, and had PR examined as exposure. The CVD outcomes were either clinical or metabolic outcomes (i.e., dyslipidemia, obesity, metabolic syndrome, hypertension, and diabetes). Results: Our literature search identified 3,800 studies, of which 17 met the inclusion criteria. Of them, seven were longitudinal and 10 cross-sectional, and 13 were on adults and four on children. The exposure assessment was heterogeneous, i.e., 12 studies used different kinds of self-administered questionnaires and five used interviews with a psychologist. Regarding outcomes, five studies investigated CVD, seven obesity, one metabolic syndrome, two hypertension, four dyslipidemia, and four diabetes. In longitudinal studies, PR was found to have an inverse association with included outcomes in five studies from the Swedish military conscription cohort but had no association with CVD in a study on African-American women and was associated with slower progression of diabetes in a general population. The cross-sectional studies showed that the prevalence of disease was not associated with PR in many cases but the progression of disease was associated with PR. Conclusion: PR seems to have a possibly favorable association with CVD and metabolic disturbances that differs according to the type of outcome and population. Our study limitations are given by the small number of studies available and the heterogeneity in PR measurement. Systematic review registration: [https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=237109], identifier [CRD42021237109]

Associations between systemic inflammation and somatic depressive symptoms: Findings from the Moli-sani study

The link between systemic inflammation and depression has been deeply investigated, but relatively few studies explored symptom-specific associations, mostly focusing on common inflammatory biomarkers like C-reactive protein (CRP) levels

Socioeconomic and psychosocial determinants of adherence to the Mediterranean diet in a general adult Italian population

To evaluate the adherence to Mediterranean diet (MD) and its major socioeconomic and psychosocial determinants in a large sample of the Italian population, covering three main geographical areas of the Country (Southern, Central and Northern)

DataSheet_1_Identifying brain tumor patients’ subtypes based on pre-diagnostic history and clinical characteristics: a pilot hierarchical clustering and association analysis.docx

IntroductionCentral nervous system (CNS) tumors are severe health conditions with increasing incidence in the last years. Different biological, environmental and clinical factors are thought to have an important role in their epidemiology, which however remains unclear.ObjectiveThe aim of this pilot study was to identify CNS tumor patients’ subtypes based on this information and to test associations with tumor malignancy.Methods90 patients with suspected diagnosis of CNS tumor were recruited by the Neurosurgery Unit of IRCCS Neuromed. Patients underwent anamnestic and clinical assessment, to ascertain known or suspected risk factors including lifestyle, socioeconomic, clinical and psychometric characteristics. We applied a hierarchical clustering analysis to these exposures to identify potential groups of patients with a similar risk pattern and tested whether these clusters associated with brain tumor malignancy.ResultsOut of 67 patients with a confirmed CNS tumor diagnosis, we identified 28 non-malignant and 39 malignant tumor cases. These subtypes showed significant differences in terms of gender (with men more frequently presenting a diagnosis of cancer; p = 6.0 ×10−3) and yearly household income (with non-malignant tumor patients more frequently earning ≥25k Euros/year; p = 3.4×10−3). Cluster analysis revealed the presence of two clusters of patients: one (N=41) with more professionally active, educated, wealthier and healthier patients, and the other one with mostly retired and less healthy men, with a higher frequency of smokers, personal history of cardiovascular disease and cancer familiarity, a mostly sedentary lifestyle and generally lower income, education and cognitive performance. The former cluster showed a protective association with the malignancy of the disease, with a 74 (14-93) % reduction in the prevalent risk of CNS malignant tumors, compared to the other cluster (p=0.026).DiscussionThese preliminary data suggest that patients’ profiling through unsupervised machine learning approaches may somehow help predicting the risk of being affected by a malignant form. If confirmed by further analyses in larger independent cohorts, these findings may be useful to create potential intelligent ranking systems for treatment priority, overcoming the lack of histopathological information and molecular diagnosis of the tumor, which are typically not available until the time of surgery.</p

Moderate Alcohol Consumption Is Associated With Lower Risk for Heart Failure But Not Atrial Fibrillation

Objectives The aim of this study was to assess the hypothesis that alcohol consumption is associated with onset of atrial fibrillation (AF) and/or heart failure (HF). Background The connection between ethanol intake and AF or HF remains controversial. Methods The study population was 22,824 AF- or HF-free subjects (48% men, age \ue2\u89\ua535 years) randomly recruited from the general population included in the Moli-sani study, for whom complete data on HF, AF, and alcohol consumption were available. The cohort was followed up to December 31, 2015, for a median of 8.2 years (183,912 person-years). Incident cases were identified through linkage to the Molise regional archive of hospital discharges. Hazard ratios were calculated using Cox proportional hazard models and cubic spline regression. Results A total of 943 incident cases of HF and 554 of AF were identified. In comparison with never drinkers, both former and occasional drinkers showed comparable risk for developing HF. Drinking alcohol in the range of 1 to 4 drinks/day was associated with a lower risk for HF, with a 22% maximum risk reduction at 20 g/day, independent of common confounders. In contrast, no association of alcohol consumption with onset of AF was observed. Very similar results were obtained after restriction of the analyses to regular or only wine drinkers or according to sex, age, social status, or adherence to the Mediterranean diet. Conclusions Consumption of alcohol in moderation was associated with a lower incidence of HF but not with development of AF

Reduced mortality risk by a polyphenol-rich diet: An analysis from the Moli-sani study

Objectives: The effect of the polyphenol content of the human diet on mortality risk is not yet fully understood. The aim of this study was to evaluate the association of a polyphenol-rich diet with mortality rate and a possible mediation effect by inflammation, in what we believe to be a novel, holistic approach. Methods: We analyzed 21 302 participants (10 980 women and 10 322 men, aged 6535 y) from the Moli-sani cohort. The participants were followed up for a median of 8.3 y. The European Prospective Investigation into Cancer and Nutrition food frequency questionnaire (FFQ) was used for dietary assessment. Flavonol, flavone, flavanone, flavanol, anthocyanin, isoflavone, and lignan intakes were calculated using European Food Information Resource\u2014Bioactive Substances in Food Information Systems and the polyphenol antioxidant content (PAC)-score was constructed to assess the total content of these nutrients in the diet. Results: Participants included in the highest quintile of intake of various polyphenol classes and subclasses presented a significant lower all-cause mortality risk compared with those in the lowest group of consumption (hazard ratio [HR] &lt; 1; P &lt; 0.05). Cox regression analyses adjusted for potential confounders indicated that participants in higher quintiles of PAC-score had lower all-cause mortality risk (HR &lt;1; P &lt; 0.05). When cause-specific mortality rates were considered, similar effects were observed for cardiocerebrovascular and cancer mortality (HR &lt;1; P &lt; 0.05). Conclusions: The polyphenol content of the diet was associated with reduced mortality risk in a Mediterranean population, possibly through an antiinflammatory mechanism