Loose ligaments are often a predisposing factor of temporomandibular joint (TMJ) disorders. This causal factor was analyzed in 701 subjects presenting at the TMJ and Posture Center of Siena University with TMJ pain or dysfunction. Along with the conventional jaw examination, a Carter and Wilkinson test as modified by Beighton was also done. We found a correlation among the parameters of age, gender, TMJ disorder, joint pain, muscle pain, and loose ligaments.

Androgen deprivation therapy (ADT) is the standard treatment of metastatic prostate cancer (PCa). However, metastases-directed therapies can delay the initiation or switch of systemic treatments and allow local control (LC) and prolonged progression-free survival (PFS), particularly in patients with lymph nodes (LN) oligometastases. We performed a systematic review on stereotactic body radiotherapy (SBRT) in this setting. Papers reporting LC and/or PFS were selected. Data on ADT-free survival, overall survival, and toxicity were also collected from the selected studies. Fifteen studies were eligible (414 patients), 14 of them were retrospective analyses. A high heterogeneity was observed in terms of patient selection and treatment. In one study SBRT was delivered as a single 20 Gy fraction, while in the others the median total dose ranged between 24 and 40 Gy delivered in 3–6 fractions. LC and PFS were reported in 15 and 12 papers, respectively. LC was reported as a crude percentage in 13 studies, with 100% rate in seven and 63.2–98.0% in six reports. Five studies reported actuarial LC (2-year LC: 70.0–100%). PFS was reported as a crude rate in 11 studies (range 27.3–68.8%). Actuarial 2-year PFS was reported in four studies (range 30.0–50.0%). SBRT tolerability was excellent, with only two patients with grade 3 acute toxicity and two patients with grade 3 late toxicity. SBRT for LN oligorecurrences from PCa in safe and provides optimal LC. However, the long-term effect on PFS and OS is still unclear as well as which patients are the best candidate for this approach.

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.

Aim: The frequent inadequacy of pain management in cancer patients is well known. Moreover, the quality of analgesic treatment in patients treated with radiotherapy (RT) has only been rarely assessed. In order to study the latter topic, we conducted a multicenter, observational and prospective study based on the Pain Management Index (PMI) in RT Italian departments. Methods: We collected data on age, gender, tumor site and stage, performance status, treatment aim, and pain (type: CP—cancer pain, NCP—non-cancer pain, MP—mixed pain; intensity: NRS: Numeric Rating Scale). Furthermore, we analyzed the impact on PMI on these parameters, and we defined a pain score with values from 0 (NRS: 0, no pain) to 3 (NRS: 7–10: intense pain) and an analgesic score from 0 (pain medication not taken) to 3 (strong opioids). By subtracting the pain score from the analgesic score, we obtained the PMI value, considering cases with values < 0 as inadequate analgesic prescriptions. The Ethics Committees of the participating centers approved the study (ARISE-1 study). Results: Two thousand one hundred four non-selected outpatients with cancer and aged 18 years or older were enrolled in 13 RT departments. RT had curative and palliative intent in 62.4% and 37.6% patients, respectively. Tumor stage was non-metastatic in 57.3% and metastatic in 42.7% of subjects, respectively. Pain affected 1417 patients (CP: 49.5%, NCP: 32.0%; MP: 18.5%). PMI was < 0 in 45.0% of patients with pain. At multivariable analysis, inadequate pain management was significantly correlated with curative RT aim, ECOG performance status = 1 (versus both ECOG-PS3 and ECOG- PS4), breast cancer, non-cancer pain, and Central and South Italy RT Departments (versus Northern Italy).Conclusions: Pain management was less adequate in patients with more favorable clinical condition and stage. Educational and organizational strategies are needed in RT departments to reduce the non-negligible percentage of patients with inadequate analgesic therapy.