Central obesity, body mass index, metabolic syndrome and mortality in Mediterranean breast cancer patients.

Obesity and metabolic disorders have been associated with poor outcomes in non-Mediterranean breast cancer (BC) patients. The purpose of this study was to investigate the prognostic potential of anthropometric variables in patients with early BC living in Southern Mediterranean region of Italy. We enrolled 955 consecutive early BC patients treated in hospitals in Naples between 2009 and 2013 (median follow-up 11.8-year ending 15/09/2022). Body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR) and metabolic syndrome (MetS) were collected. All-cause and BC-specific mortality were calculated. At the last day of contact 208 (22%) patients had died, 131 (14%) from BC. High WC (≥ 88 cm) or WHR (> 0.85) and the MetS were significantly associated with moderately increased risk of all-cause mortality (HR=1.39, 1.62, 1.61, respectively). A significant increased risk of BC-specific mortality was found in obese patients, in those with high WC, high WHR and those with MetS (HR=1.72, 1.71, 1.80, 1.81, respectively). Central obesity significantly increased total and BC-specific mortality particularly in pre-menopausal women and in luminal subtypes, while in post-menopause MetS was a stronger risk factor. Obesity and MetS may impair the effectiveness of BC therapies hence active lifestyle interventions are encouraged.

Utilizing an artificial intelligence framework (conditional generative adversarial network) to enhance telemedicine strategies for cancer pain management.

BACKGROUND: The utilization of artificial intelligence (AI) in healthcare has significant potential to revolutionize the delivery of medical services, particularly in the field of telemedicine. In this article, we investigate the capabilities of a specific deep learning model, a generative adversarial network (GAN), and explore its potential for enhancing the telemedicine approach to cancer pain management. MATERIALS AND METHODS: We implemented a structured dataset comprising demographic and clinical variables from 226 patients and 489 telemedicine visits for cancer pain management. The deep learning model, specifically a conditional GAN, was employed to generate synthetic samples that closely resemble real individuals in terms of their characteristics. Subsequently, four machine learning (ML) algorithms were used to assess the variables associated with a higher number of remote visits. RESULTS: The generated dataset exhibits a distribution comparable to the reference dataset for all considered variables, including age, number of visits, tumor type, performance status, characteristics of metastasis, opioid dosage, and type of pain. Among the algorithms tested, random forest demonstrated the highest performance in predicting a higher number of remote visits, achieving an accuracy of 0.8 on the test data. The simulations based on ML indicated that individuals who are younger than 45 years old, and those experiencing breakthrough cancer pain, may require an increased number of telemedicine-based clinical evaluations. CONCLUSION: As the advancement of healthcare processes relies on scientific evidence, AI techniques such as GANs can play a vital role in bridging knowledge gaps and accelerating the integration of telemedicine into clinical practice. Nonetheless, it is crucial to carefully address the limitations of these approaches.

[What affects diagnostic and therapeutic times for cancer patients care?] / Cosa influisce sui tempi diagnostici e terapeutici dei pazienti oncologici?

INTRODUCTION: According with "Numbers of cancer in Italy. 2021" mortality is decreasing for both the genders (-10% for men, -8% for women) in Italy. However, this trend is not uniform and seems stable in the Southern regions. Analyses of oncological care in Campania Region highlighted some structural critical issues and delays, which did not guarantee an efficient and effective use of the available economic resources. So, the Campania region established in September 2016 the Campania oncological network (Roc) addressed to prevention, diagnosis, treatment and rehabilitation of tumours through the establishment of multidisciplinary oncological groups (Gom). In February 2020, the ValPeRoc project was launched with the aim of periodically and progressively evaluating the Roc's performance both for the clinical services and for the economic aspects. METHODS: In five Goms (colon, ovary, lung, prostate, bladder) active in some Roc hospitals, the pre-Gom time elapsing between the date of diagnosis and the date of the first Gom meeting and the Gom time elapsing between the date of the first Gom meeting and the date of the treatment decision were measured. Gom times longer than 28 days were defined as high. The risk of high Gom time was analyzed with a Bart-type machine learning algorithm, considering the set of regressors (features) available to classify patients. RESULTS: The results on the test set (54 patients) report an accuracy of 0.68. The classification technique reported a good fit for colon Gom (93%) and an over-classification for lung Gom. The study of the marginal effects showed a higher risk for those who had a previous therapeutic act and for lung Gom. CONCLUSIONS: Within the Goms took in consideration the proposed statistical technique showed that, depending on each Gom, correctly classified about 70% of individuals on risk of delaying permanence within the Roc. The ValPeRoc project evaluates Roc activity for the first time through a replicable analysis of patient pathway times from diagnosis to the act of treatment. Specifically, the times analyzed measure the quality of the regional health care system.

Implementation of a Hybrid Care Model for Telemedicine-based Cancer Pain Management at the Cancer Center of Naples, Italy: A Cohort Study.

BACKGROUND/AIM: Telemedicine, the remote delivery of healthcare services, represents a great opportunity for cancer pain management. A care model of telemedicine that combines remote visits and hospital access could be an effective and safe strategy for pain management of cancer patients. PATIENTS AND METHODS: A retrospective study was conducted using the dataset of the telemedicine program at the Istituto Nazionale Tumori of Naples, Italy for assessing the efficacy of a telehealth-based model of care. Demographic, clinical, and process variables were investigated. RESULTS: A total of 226 cases and 489 visits were included in the analysis. The mean age of patients was 63.4 years (SD=12.4 years), and no sex differences were observed. Approximately 55% of patients were ECOG-PS ≤2 and 87% suffered from metastatic disease. More than half of the patients were treated with high doses of opioids. Each patient had a mean of 2 remote visits and half of the patients had more than 1 telehealth consultation. The dropout ratio was 5.3%. Most visits (n=472) were conducted on patients in the Campania Region, Italy. The maximum covered distance from the Cancer Center and the patients' location was 555.22 Km. A significant difference in the overall number of visits (p=0.006) and the number of pro-capita remote visits (p=0.010) was found, in favor of the group of patients treated before the end of the Covid-19 emergency in Italy, compared to those treated after the pandemic. CONCLUSION: Despite various positive outcomes, the analysis highlights several weaknesses, such as the need to assist patients with advanced and non-advanced disease located outside the regional territory. Overall, the telehealth processes should be adapted to post-pandemic scenarios towards their implementation in routine clinical practice.

A comparison of simplified protocols of personalized dosimetry in NEN patients treated by radioligand therapy (RLT) with [177Lu]Lu-DOTATATE to favor its use in clinical practice.

The role of internal dosimetry is usually proposed for investigational purposes in patients treated by RLT, even if its application is not yet the standard method in clinical practice. This limited use is partially justified by several concomitant factors that make calculations a complex process. Therefore, simplified dosimetry protocols are required. METHODS: In our study, dosimetric evaluations were performed in thirty patients with NENs who underwent RLT with [177Lu]Lu-DOTATATE. The reference method (M0) calculated the cumulative absorbed dose performing dosimetry after each of the four cycles. Obtained data were employed to assess the feasibility of simplified protocols: defining the dosimetry only after the first cycle (M1) and after the first and last one (M2). RESULTS: The mean differences of the cumulative absorbed doses between M1 and M0 were - 10% for kidney, - 5% for spleen, + 34% for liver, + 13% for red marrow, and + 37% for tumor lesions. Conversely, differences lower than ± 10% were measured between M2 and M0. CONCLUSION: Cumulative absorbed doses obtained with the M2 protocol resembled the doses calculated by M0, while the M1 protocol overestimated the absorbed doses in all organs at risk, except for the spleen.

Bayesian Network Analysis for Prediction of Unplanned Hospital Readmissions of Cancer Patients with Breakthrough Cancer Pain and Complex Care Needs.

BACKGROUND: Unplanned hospital readmissions (HRAs) are very common in cancer patients. These events can potentially impair the patients' health-related quality of life and increase cancer care costs. In this study, data-driven prediction models were developed for identifying patients at a higher risk for HRA. METHODS: A large dataset on cancer pain and additional data from clinical registries were used for conducting a Bayesian network analysis. A cohort of gastrointestinal cancer patients was selected. Logical and clinical relationships were a priori established to define and associate the considered variables including cancer type, body mass index (BMI), bone metastasis, serum albumin, nutritional support, breakthrough cancer pain (BTcP), and radiotherapy. RESULTS: The best model (Bayesian Information Criterion) demonstrated that, in the investigated setting, unplanned HRAs are directly related to nutritional support (p = 0.05) and radiotherapy. On the contrary, BTcP did not significantly affect HRAs. Nevertheless, the correlation between variables showed that when BMI ≥ 25 kg/m2, the spontaneous BTcP is more predictive for HRAs. CONCLUSIONS: Whilst not without limitations, a Bayesian model, combined with a careful selection of clinical variables, can represent a valid strategy for predicting unexpected HRA events in cancer patients. These findings could be useful for calibrating care interventions and implementing processes of resource allocation.

Is There Indication for the Use of Biological Mesh in Cancer Patients?

Up to 28% of all patients who undergo open surgery will develop a ventral hernia (VH) in the post-operative period. VH surgery is a debated topic in the literature, especially in oncological patients due to complex management. We searched in the surgical database of the Hepatobiliary Unit of the National Cancer Institute of Naples "G. Pascale Foundation" for all patients who underwent abdominal surgery for malignancy from January 2010 to December 2018. Our surgical approach and our choice of mesh for VH repair was planned case-by-case. We selected 57 patients that fulfilled our inclusion criteria, and we divided them into two groups: biological versus synthetic prosthesis. Anterior component separation was used in 31 patients (54.4%) vs. bridging procedure in 26 (45.6%). In 41 cases (71.9%), we used a biological mesh while a synthetic one was adopted in the remaining patients. Of our patients, 57% were male (33 male vs. 24 female) with a median age of 65 and a mean BMI of 30.8. We collected ventral hernia defects from 35 cm2 to 600 cm2 (mean 205.2 cm2); 30-day complications were present in 24 patients (42.1%), no 30-day mortality was reported, and 21 patients had a recurrence of pathology during study follow-up. This study confirms VH recurrence risk is not related with the type of mesh but is strongly related with BMI and type of surgery also in oncological patients.

Different Machine Learning Approaches for Implementing Telehealth-Based Cancer Pain Management Strategies.

Background: The most effective strategy for managing cancer pain remotely should be better defined. There is a need to identify those patients who require increased attention and calibrated follow-up programs. Methods: Machine learning (ML) models were developed using the data prospectively obtained from a single-center program of telemedicine-based cancer pain management. These models included random forest (RF), gradient boosting machine (GBM), artificial neural network (ANN), and the LASSO−RIDGE algorithm. Thirteen demographic, social, clinical, and therapeutic variables were adopted to define the conditions that can affect the number of teleconsultations. After ML validation, the risk analysis for more than one remote consultation was assessed in target individuals. Results: The data from 158 patients were collected. In the training set, the accuracy was about 95% and 98% for ANN and RF, respectively. Nevertheless, the best accuracy on the test set was obtained with RF (70%). The ML-based simulations showed that young age (<55 years), lung cancer, and occurrence of breakthrough cancer pain help to predict the number of remote consultations. Elderly patients (>75 years) with bone metastases may require more telemedicine-based clinical evaluations. Conclusion: ML-based analyses may enable clinicians to identify the best model for predicting the need for more remote consultations. It could be useful for calibrating care interventions and resource allocation.

Satisfaction with Telemedicine for Cancer Pain Management: A Model of Care and Cross-Sectional Patient Satisfaction Study.

Background: Since cancer pain requires complex modalities of care, the proper strategy for addressing its telemedicine-based management should be better defined. This study aimed to trace a pathway for a progressive implementation of the telemedicine process for the treatment of pain in the setting of cancer patients. Methods: The features of the model were investigated to dissect the dropout from the telemedicine pathway. A cross-sectional patient satisfaction study was conducted. The degree of satisfaction was evaluated through a developed 22-item questionnaire (Likert scale 0−7). Results: A total of 375 video consultations for 164 patients (mean age 62.9 years) were performed through remote consultations for cancer pain management between March 2021 and February 2022. After the exclusion of 72 patients, 92 (56.1%) were included in the analysis. The dropout ratio was 8.7%. The number of visits and pharmacological therapies for neuropathic pain correlated with the risk for readmission (p < 0.05). Overall, the satisfaction was very high (mean > 5.5 for all items). Conclusion: Feedback from patients reflected high satisfaction rates with the care provided. A methodological approach based on the degree of satisfaction combined with the analysis of the pathways can help to implement the quality of a service provided through telemedicine. While not without limitations, our hybrid protocol can be useful for addressing cancer pain through a patient-centered approach.

A SARS-CoV-2 Infection High-Uptake Program on Healthcare Workers and Cancer Patients of the National Cancer Institute of Naples, Italy.

BACKGROUND: From the beginning of 2020, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) quickly spread worldwide, becoming the main problem for the healthcare systems. Healthcare workers (HCWs) are at higher risk of infection and can be a dangerous vehicle for the spread of the virus. Furthermore, cancer patients (CPs) are a vulnerable population, with an increased risk of developing severe and lethal forms of Coronavirus Disease 19 (COVID-19). Therefore, at the National Cancer Institute of Naples, where only cancer patients are treated, a surveillance program aimed to prevent the hospital access of SARS-CoV-2 positive subjects (HCWs and CPs) was implemented. The study aims to describe the results of the monitoring activity for the SARS-CoV-2 spread among HCWs and CPs, from March 2020 to March 2021. METHODS: This surveillance program included a periodic sampling through nasopharyngeal molecular swabs for SARS-CoV-2 (Real-Time Polymerase Chain Reaction, RT-PCR). CPs were submitted to the molecular test at least 48 h before hospital admission. Survival analysis and multiple logistic regression models were performed among HCWs and CPs to assess the main SARS-CoV-2 risk factors. RESULTS: The percentages of HCWs tested with RT-PCR for the detection of SARS-CoV-2, according to the first and the second wave, were 79.7% and 91.7%, respectively, while the percentages for the CPs were 24.6% and 39.6%. SARS-CoV-2 was detected in 20 (1.7%) HCWs of the 1204 subjects tested during the first wave, and in 127 (9.2%) of 1385 subjects tested in the second wave (p < 0.001); among CPs, the prevalence of patients tested varied from 100 (4.6%) during the first wave to 168 (4.9%) during the second wave (p = 0.8). The multivariate logistic analysis provided a significant OR for nurses (OR = 2.24, 95% CI 1.23-4.08, p < 0.001) compared to research, administrative staff, and other job titles. CONCLUSIONS: Our findings show that the positivity rate between the two waves in the HCWs increased over time but not in the CPs; therefore, the importance of adopting stringent measures to contain the shock wave of SARS-CoV-2 infection in the hospital setting was essential. Among HCWs, nurses are more exposed to contagion and patients who needed continuity in oncological care for diseases other than COVID-19, such as suspected cancer.

Bibliometric Network Analysis on Rapid-Onset Opioids for Breakthrough Cancer Pain Treatment.

BACKGROUND AND OBJECTIVES: Proper breakthrough cancer pain (BTcP) management is of pivotal importance. Although rapid-acting, oral and nasal transmucosal, fentanyl formulations (rapid-onset opioids, ROOs) are licensed for BTcP treatment, not all guidelines recommend their use. Presumably, some research gaps need to be bridged to produce solid evidence. We present a bibliometric network analysis on ROOs for BTcP treatment. METHODS: Documents were retrieved from the Web of Science (WOS) online database. The string was "rapid onset opioids" or "transmucosal fentanyl" and "breakthrough cancer pain". Year of publication, journal metrics (impact factor and quartile), title, document type, topic, and clinical setting (in-patients, outpatients, and palliative care) were extracted. The software tool VOSviewer (version 1.6.17) was used to analyze the semantic network analyzes, bibliographic coupling, journals analysis, and research networks. RESULTS: 502 articles were found in WOS. A declining trend in published articles from 2014 to 2021 was observed. Approximately 50% of documents regard top quartile (Q1) journals. Most articles focused on ROOs efficacy, but abuse and misuse issues are poorly addressed. With respect to article type, we calculated 132 clinical investigations. The semantic network analysis found interconnections between the terms "breakthrough cancer pain," "opioids," and "cancers." The top co-cited article was published in 2000 and addressed pain assessment. The largest number of partnerships regarded the United States, Italy, and England. CONCLUSION: In this research area, most articles are published in top-ranked journals. Nevertheless, paramount topics should be better addressed, and the implementation of research networks is needed.

New approach to implement cancer patient care: The valutazione percorso rete oncologica campana (ValPeROC)-experience from an Italian oncology network.

OBJECTIVE: The primary goal of the Campania Oncology Network (ROC) was to reduce cancer delay and care fragmentation through the establishment of cancer-specific multidisciplinary oncologic groups (GOMs) and diagnostic and therapeutic assistance paths (PDTAs). METHODS: Five cancer centres of the ROC, with their own cancer specific GOM, were selected. In our analysis, we have focused on four neoplasms: lung, colon, ovarian and prostate cancers. The median time for pre-GOM and GOM Times was calculated for each tumour site. Univariate and multivariate logistic regressions were performed to individuate risk factors for pre-GOM and GOM Time. RESULTS: Significant differences were observed for prostate cancer compared to other patients either for pre-GOM or GOM Times. Significant risks were found for ovarian and prostate cancers in pre-GOM time and for prostate cancer in GOM-Time. CONCLUSIONS: This experience will produce knowledge and data to guide decision-making and to manage more effectively the challenges of fighting cancer in Campania region. The Valutazione Percorso Rete Oncologica Campana (ValPeROC) study evaluates, for the first time, the ROC activity, through the analysis of key performance indices. Pre-GOM and GOM Time represent the quality of the entire regional health system and are useful to define models, which can evaluate the performance of the ROC over time.

Multidimensional Statistical Technique for Interpreting the Spontaneous Breakthrough Cancer Pain Phenomenon. A Secondary Analysis from the IOPS-MS Study.

Breakthrough cancer pain (BTcP) is a temporary exacerbation of pain that "breaks through" a phase of adequate pain control by an opioid-based therapy. The non-predictable BTcP (NP-BTcP) is a subtype of BTcP that occurs in the absence of any specific activity. Since NP-BTcP has an important clinical impact, this analysis is aimed at characterizing the NP-BTcP phenomenon through a multidimensional statistical technique. This is a secondary analysis based on the Italian Oncologic Pain multiSetting-Multicentric Survey (IOPS-MS). A correlation analysis was performed to characterize the NP-BTcP profile about its intensity, number of episodes per day, and type. The multiple correspondence analysis (MCA) determined the identification of four groups (phenotypes). A univariate analysis was performed to assess differences between the four phenotypes and selected covariates. The four phenotypes represent the hierarchical classification according to the status of NP-BTcP: from the best (phenotype 1) to the worst (phenotype 4). The univariate analysis found a significant association between the onset time >10 min in the phenotype 1 (37.3%)' vs. the onset > 10 min in phenotype 4 (25.8%) (p < 0.001). Phenotype 1 was characterized by the gastrointestinal type of cancer (26.4%) with respect to phenotype 4, where the most frequent cancer affected the lung (28.8%) (p < 0.001). Phenotype 4 was mainly managed with rapid-onset opioids, while in phenotype 1, many patients were treated with oral, subcutaneous, or intravenous morphine (56.4% and 44.4%, respectively; p = 0.008). The ability to characterize NP-BTcP can offer enormous benefits for the management of this serious aspect of cancer pain. Although requiring validation, this strategy can provide many indications for identifying the diagnostic and therapeutic gaps in NP-BTcP management.

Changes in Lifestyle and Dietary Habits during COVID-19 Lockdown in Italy: Results of an Online Survey.

COVID-19 is an unprecedented global pandemic. On 12 March 2020, a lockdown order was issued in Italy in attempt to contain the health crisis. The study aimed to assess the impact of the COVID-19 lockdown on diet, physical activity, sleep quality, and distress in an Italian cohort. An online anonymous interview, which included validated questionnaires was created to compare lifestyle habits pre- and during the lockdown. Data analysis from 604 subjects with a mean age of 29.8 years was carried out using multivariate analysis. Compared to pre-COVID-19 times, 67% of people changed their eating habits and increased consumption of foods containing added sugars. Women and men with low adherence to the Mediterranean Diet (MedDiet) were more likely to be physically inactive (p < 0.0001 and p < 0.01, respectively). Results from logistic regression showed a three times higher risk of being inactive if adherence to the MedDiet was low (p < 0.0001), especially in men between 26 and 35 years. Lower levels of distress were reported in males who were physically active (89%) (p < 0.001). Our findings may help to identify effective lifestyle interventions during restrictive conditions.

Quality of Life in Women Diagnosed with Breast Cancer after a 12-Month Treatment of Lifestyle Modifications.

Healthy lifestyles are associated with better health-related quality of life (HRQoL), favorable prognosis and lower mortality in breast cancer (BC) survivors. We investigated changes in HRQoL after a 12-month lifestyle modification program in 227 BC survivors participating in DEDiCa trial (Mediterranean diet, exercise, vitamin D). HRQoL was evaluated through validated questionnaires: EQ-5D-3L, EORTC-QLQ-C30 and EORTC QLQ-BR23. Baseline changes were tested using analysis of variance. Multiple regression analyses were performed to assess treatment effects on HRQoL. Increases were observed in global health status (p < 0.001), physical (p = 0.003), role (p = 0.002) and social functioning (p < 0.001), body image (p < 0.001), future perspective (p < 0.001), well-being (p = 0.001), and reductions in fatigue (p < 0.001), nausea and vomiting (p = 0.015), dyspnea (p = 0.001), constipation (p = 0.049), financial problems (p = 0.012), sexual functioning (p = 0.025), systematic therapy side effects (p < 0.001) and breast symptoms (p = 0.004). Multiple regression analyses found inverse associations between changes in BMI and global health status (p = 0.048) and between serum 25(OH)D levels and breast symptoms (p = 0.002). A healthy lifestyle treatment of traditional Mediterranean diet and exercise may impact positively on HRQoL in BC survivors possibly through reductions in body weight while vitamin D sufficiency may improve BC-related symptoms. These findings are relevant to BC survivors whose lower HRQoL negatively affects treatment compliance and disease outcomes.