Bipolar Patients and Bullous Pemphigoid after Risperidone Long-Acting Injectable: A Case Report and a Review of the Literature

Neuropsychiatric disorders are found to be associated with bullous pemphigoid (BP), an autoimmune subepidermal blistering disease. Antipsychotics have emerged as possible inducing factors of BP. However, large sample studies concerning BP associated with antipsychotics, as well as with specific mental disorders, are still lacking. Our review retrieved a few clinical studies and case reports on the topic, producing controversial results. We report for the first time a bipolar patient case presenting BP following five-month therapy with risperidone long-acting injectable (LAI). We hypothesize that the dermatological event is associated with the medication administered. The issue emerged during psychiatric consultation and was confirmed by histological examination, direct and indirect immunofluorescence studies, plus positive plasma and cutaneous BP180 and BP230 IgG. Neurodegeneration or neuroinflammation might represent a primary process leading to a cross-reactive immune response between neural and cutaneous antigens and contributing to self-tolerance failure. Furthermore, the time sequence of the shared biological mechanisms leading to clinical manifestations of the neuropsychiatric disorder and BP remains undefined. BP comorbid with bipolar disorder might occasionally represent a serious health risk and affect patients’ physical and psychosocial quality of life. Thus, clinicians treating psychiatric patients should consider BP as a possible adverse effect of psychotropic medications

A SHort course Accelerated RadiatiON therapy (SHARON) dose-escalation trial in older adults head and neck non-melanoma skin cancer

Objectives: To assess feasibility and safety of a SHort-course Accelerated RadiatiON therapy (SHARON) regimen, in the treatment of non-melanoma skin cancers (NMSC) in older patients.Methods: Old patients (age >= 80 years) with histological confirmed non-melanoma skin cancers were enrolled. The primary endpoint was to determine the maximum tolerated dose (MTD). Radiotherapy regimen was based on the delivery of four radiotherapy fractions (5 Gy per fraction) with a twice daily fractionation in two consecutive days, Three different level of dose were administered: 20 Gy (one cycle), 40 Gy (two cycles) and 60 Gy (three cycles).Results: Thirty patients (median age: 91 years; range: 80-96) were included in this analysis, Among fourteen patients who completed the one cycle, only one (7%) experimented acute G4 skin toxicity. Twelve patients reported an improvement or resolution of baseline symptoms (overall palliative response rate: 85.8%). Nine and seven patients underwent to two and three RT cycles, respectively: of these, no G3 toxicities were recorded. The overall response rate was 100% when three cycles were delivered. The overall six-month symptom-free survival was 787% and 77.8% in patients treated with one course and more courses, respectively.Conclusions: Short-course accelerated radiotherapy in older patients with non-melanoma skin cancers is well tolerated. High doses seem to be more effective in terms of response rate.Advances in knowledge: This approach could represent an option for older adults with NMSC, being both palliative (one course) or potentially curative (more courses) in the aim, accordingly to the patient's condition

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

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

AIRO Breast Cancer Group Best Clinical Practice 2022 Update

Introduction: Breast cancer is the most common tumor in women and represents the leading cause of cancer death. Radiation therapy plays a key-role in the treatment of all breast cancer stages. Therefore, the adoption of evidence-based treatments is warranted, to ensure equity of access and standardization of care in clinical practice.Method: This national document on the highest evidence-based available data was developed and endorsed by the Italian Association of Radiation and Clinical Oncology (AIRO) Breast Cancer Group.We analyzed literature data regarding breast radiation therapy, using the SIGN (Scottish Intercollegiate Guidelines Network) methodology (www.sign.ac.uk). Updated findings from the literature were examined, including the highest levels of evidence (meta-analyses, randomized trials, and international guidelines) with a significant impact on clinical practice. The document deals with the role of radiation therapy in the treatment of primary breast cancer, local relapse, and metastatic disease, with focus on diagnosis, staging, local and systemic therapies, and follow up. Information is given on indications, techniques, total doses, and fractionations.Results: An extensive literature review from 2013 to 2021 was performed. The work was organized according to a general index of different topics and most chapters included individual questions and, when possible, synoptic and summary tables. Indications for radiation therapy in breast cancer were examined and integrated with other oncological treatments. A total of 50 questions were analyzed and answered.Four large areas of interest were investigated: (1) general strategy (multidisciplinary approach, contraindications, preliminary assessments, staging and management of patients with electronic devices); (2) systemic therapy (primary, adjuvant, in metastatic setting); (3) clinical aspects (invasive, non-invasive and micro-invasive carcinoma; particular situations such as young and elderly patients, breast cancer in males and cancer during pregnancy; follow up with possible acute and late toxicities; loco-regional relapse and metastatic disease); (4) technical aspects (radiation after conservative surgery or mastectomy, indications for boost, lymph node radiotherapy and partial breast irradiation).Appendixes about tumor bed boost and breast and lymph nodes contouring were implemented, including a dedicated web application. The scientific work was reviewed and validated by an expert group of breast cancer key-opinion leaders.Conclusions: Optimal breast cancer management requires a multidisciplinary approach sharing therapeutic strategies with the other involved specialists and the patient, within a coordinated and dedicated clinical path. In recent years, the high-level quality radiation therapy has shown a significant impact on local control and survival of breast cancer patients. Therefore, it is necessary to offer and guarantee accurate treatments according to the best standards of evidence-based medicine

Psychological Wellbeing and Healthy Aging: Focus on Telomeres

Stress and depression are known to modulate the aging process, and might also affect telomere biology. In fact, exposure to some biochemical pathways involved in stress-related depression may contribute to an ‘‘accelerated aging„ phenotype, as well as the incidence of age-related diseases, including metabolic disorders and dementia. Basic studies support the notion that the telomere and telomerase system plays a pivotal role in the aging process and disease promotion. Interestingly, short and dysfunctional telomeres are associated with reduced lifespan, as shown in animal models. In this context, telomeres are very sensitive to stress, mindset, and lifestyle, and their rescue may be sufficient to restore cell and organism viability. This mini-review discusses conceptual models of healthy and active aging and their relationship with telomere biology and mental health