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Background: A strong relationship has been observed between comorbidities and the risk of severe/fatal COVID‐19 manifestations, but no score is available to evaluate their association in cancer patients. To make up for this lacuna, we aimed to develop a comorbidity score for cancer patients, based on the Lombardy Region healthcare databases. Methods: We used hospital discharge records to identify patients with a new diagnosis of solid cancer between February and December 2019; 61 comorbidities were retrieved within 2 years before cancer diagnosis. This cohort was split into training and validation sets. In the training set, we used a LASSO‐logistic model to identify comorbidities associated with the risk of developing a severe/fatal form of COVID‐19 during the first pandemic wave (March–May 2020). We used a logistic model to estimate comorbidity score weights and then we divided the score into five classes (<=−1, 0, 1, 2–4, >=5). In the validation set, we assessed score performance by areas under the receiver operating characteristic curve (AUC) and calibration plots. We repeated the process on second pandemic wave (October–December 2020) data. Results: We identified 55,425 patients with an incident solid cancer. We selected 21 comorbidities as independent predictors. The first four score classes showed similar probability of experiencing the outcome (0.2% to 0.5%), while the last showed a probability equal to 5.8%. The score performed well in both the first and second pandemic waves: AUC 0.85 and 0.82, respectively. Our results were robust for major cancer sites too (i.e., colorectal, lung, female breast, and prostate). Conclusions: We developed a high performance comorbidity score for cancer patients and COVID‐19. Being based on administrative databases, this score will be useful for adjusting for comorbidity confounding in epidemiological studies on COVID‐19 and cancer impact. [ABSTRACT FROM AUTHOR]

Our aim was to analyse, on a population level, the year‐long decline in cancer diagnoses in the region of Lombardy (Italy), and to characterise the tumours with the greatest reduction in diagnosis by patient age, sex and tumour stage at diagnosis. We used the health care utilisation databases of the Lombardy region to identify cancer patients' characteristics (eg, sex, age) and cancer‐related information (eg, cancer site, stage at diagnosis). The frequency of new cancer diagnoses in 2019 and 2020 were compared in terms of percentage differences in undiagnosed cases. We observed two peaks in the decline in cancer diagnoses: March to May 2020 (−37%) and October to December 2020 (−19%). The decline persisted over the course of 2020 and was higher in males and patients aged 74+. Diagnoses of all four common cancers analysed (female breast, lung, colorectal and prostate) remained below pre‐pandemic levels. For breast and colorectal cancers, the decline in diagnoses was high in the age groups targeted by population‐based screening programmes. We observed a reduction in localised stage cancer diagnoses for all four cancers. Our data confirm that timely monitoring of cancer diagnoses and interventions to prevent disruption of routine diagnostic services are needed to mitigate the impact of emergencies on cancer patients. [ABSTRACT FROM AUTHOR]

Background: Evidence about late effects in adolescent and young adult (AYA) cancer survivors is scarce. This study assessed the risk of subsequent malignant neoplasms (SMNs) to identify the most common SMNs to be considered in follow‐up care. Methods: Population‐based cancer registries retrospectively identified first primary tumors (between 1976 and 2013) and SMNs in AYAs (15‐39 years old at their cancer diagnosis). AYA cancer survivors were those alive at least 5 years after their first cancer diagnosis. The excess risk of SMNs was measured as standardized incidence ratios (SIRs) and absolute excess risk together with the cumulative incidence of SMNs. Results: The cohort included 67,692 AYA cancer survivors. The excess risk of developing any SMN (SIR, 1.6; 95% confidence interval, 1.5‐1.7) was 60%. The excess risk of SMNs was significantly high for survivors of lymphomas; cancers of the breast, thyroid, female genital tract, digestive organs, gonads, and urinary tract; and melanomas. The cumulative incidence of all SMNs in AYA cancer survivors within 25 years of their first cancer diagnosis was approximately 10%. Subsequent tumors contributing to approximately 60% of all SMNs were breast cancer, colorectal cancer, corpus uteri cancer, and ovarian cancer in females and colorectal cancer, bladder cancer, prostate cancer, lung cancer, and lymphomas in males. Conclusions: These results highlight the need to personalize follow‐up strategies for AYA cancer survivors. Most subsequent malignant neoplasms (SMNs) in adolescent and young adult cancer survivors are preventable. The results of this study call for dedicated interventions (eg, behavioral change interventions and surveillance) aimed at minimizing the number of SMNs in these patients. [ABSTRACT FROM AUTHOR]

Background: Despite advances in treatments, 30% to 50% of stage III‐IV head and neck squamous cell carcinoma (HNSCC) patients relapse within 2 years after treatment. The Big Data to Decide (BD2Decide) project aimed to build a database for prognostic prediction modeling. Methods: Stage III‐IV HNSCC patients with locoregionally advanced HNSCC treated with curative intent (1537) were included. Whole transcriptomics and radiomics analyses were performed using pretreatment tumor samples and computed tomography/magnetic resonance imaging scans, respectively. Results: The entire cohort was composed of 71% male (1097)and 29% female (440): oral cavity (429, 28%), oropharynx (624, 41%), larynx (314, 20%), and hypopharynx (170, 11%); median follow‐up 50.5 months. Transcriptomics and imaging data were available for 1284 (83%) and 1239 (80%) cases, respectively; 1047 (68%) patients shared both. Conclusions: This annotated database represents the HNSCC largest available repository and will enable to develop/validate a decision support system integrating multiscale data to explore through classical and machine learning models their prognostic role. [ABSTRACT FROM AUTHOR]

Cancer types in adolescents and young adults form a unique distribution. A system for classifying them is presented. [ABSTRACT FROM AUTHOR]

Geographical variability of cancer burden was almost exclusively estimated for common cancers. Since rare cancers (RC) have become an area of priority for basic and clinical research and public health organizations, this paper provides, using a common methodology, a detailed comparison of incidence and survival for RC in the US and Europe. We estimated incidence and net survival of 199 malignant RC from data of 2 580 000 patients collected by 18 US‐SEER and 94 European registries, diagnosed within the most recent common period 2000‐2007. RC were defined according to the criterion of crude annual incidence rates <6/100 000. In total, 196 RC were classified as rare in both populations. Of these, 43 had incidence rates significantly different by at least 0.2 per 100 000:34 higher in the US and 9 higher in Europe. Five‐year net survival for all RC combined significantly differed: 54% in the US and 48% in Europe. Survival for 62 RC was significantly higher in the US vs 6 higher in Europe. Differences were not concentrated in a particular cancer family, and were mostly relevant for cases diagnosed >65+ years of age. Use of standardized methods evidenced that incidence and survival rate of majority of RC were higher in the United States compared to Europe. Possible reasons for such differences, requiring further studies, include distribution of risk factors, ability to diagnose RC, different registration practices, and use of updated International Classification of Diseases for Oncology. [ABSTRACT FROM AUTHOR]

Background: Malignant pleural mesothelioma (MPM) is a rare cancer with a poor prognosis. Centralization of rare cancer in dedicated centers is recommended to ensure expertise, multidisciplinarity and access to innovation. In Italy, expert centers for MPM have not been identified in all regions. We aimed to describe the treatment patterns among MPM patients across different Italian regions and to identify factors associated with the treatment patterns across the regions. Methods: We performed an observational study on a random sample of 2026 MPM patients diagnosed in 2003–2008. We included 26 population‐based registries covering 70% of the Italian population. To identify factors associated with treatment patterns, across the different regions, we fitted a multinomial logistic regression model adjusted by age, sex, stage, histology and hospital with thoracic surgical department. Results: MPM patients mostly received chemotherapy alone (41%) or no cancer‐directed therapy (36%) especially the older patients. The first course of treatment for MPM patients differed across regions. Patients from Piedmont, Liguria and Campania were more likely to receive no cancer‐directed therapy; those living in Tuscany and Sicily were more likely to get surgery; patients from Marche and Lazio were more likely to receive chemotherapy. These differences were not explained by age, sex, stage, histology and availability of a thoracic surgery department. Conclusions: There is limited expertise available and lack of a network able to maximize the expertise available may contribute to explaining the results of our study. Our findings support the need to ensure the appropriate care of all MPM patients in reorganizing the health care services. Key points: Significant findings of the study: MPM patients mostly received chemotherapy alone or no cancer‐directed therapy especially the older patients. The first course of treatment for MPM patients differed across Italian regions. What this study adds: Differences in MPM clinical management are not explained by the age, stage, histology nor by the availability of a thoracic surgery department. Limited expertise for MPM contribute to explaining the unequal access to appropriate care for MPM patients in Italy. [ABSTRACT FROM AUTHOR]