Head and neck radiotherapy amid the COVID‑19 pandemic: practice recommendations of the Italian Association of Radiotherapy and Clinical Oncology (AIRO)

Abstract Management of patients with head and neck cancers (HNCs) is challenging for the Radiation Oncologist, especially in the COVID-19 era. The Italian Society of Radiotherapy and Clinical Oncology (AIRO) identified the need of practice recommendations on logistic issues, treatment delivery and healthcare personnel’s protection in a time of limited resources. A panel of 15 national experts on HNCs completed a modified Delphi process. A five-point Likert scale was used; the chosen cut-offs for strong agreement and agreement were 75% and 66%, respectively. Items were organized into two sections: (1) general recommendations (10 items) and (2) special recommendations (45 items), detailing a set of procedures to be applied to all specific phases of the Radiation Oncology workflow. The distribution of facilities across the country was as follows: 47% Northern, 33% Central and 20% Southern regions. There was agreement or strong agreement across the majority (93%) of proposed items including treatment strategies, use of personal protection devices, set-up modifications and follow-up re-scheduling. Guaranteeing treatment delivery for HNC patients is well-recognized in Radiation Oncology. Our recommendations provide a flexible tool for management both in the pandemic and post-pandemic phase of the COVID-19 outbreak

Partial prostate re-irradiation for the treatment of isolated local recurrence of prostate cancer in patients previously treated with primary external beam radiotherapy: short-term results of a monocentric study

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Salvage stereotactic body radiotherapy for isolated lymph node recurrent prostate cancer : single institution series of 94 consecutive patients and 124 lymph nodes

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Cure indicators and prevalence by stage at diagnosis for breast and colorectal cancer patients: A population‐based study in Italy

People alive many years after breast (BC) or colorectal cancer (CRC) diagnoses are increasing. This paper aimed to estimate the indicators of cancer cure and complete prevalence for Italian patients with BC and CRC by stage and age. A total of 31 Italian Cancer Registries (47% of the population) data until 2017 were included. Mixture cure models allowed estimation of net survival (NS); cure fraction (CF); time to cure (TTC, 5-year conditional NS >95%); cure prevalence (who will not die of cancer); and already cured (prevalent patients living longer than TTC). 2.6% of all Italian women (806,410) were alive in 2018 after BC and 88% will not die of BC. For those diagnosed in 2010, CF was 73%, 99% when diagnosed at stage I, 81% at stage II, and 36% at stages III-IV. For all stages combined, TTC was >10 years under 45 and over 65 years and for women with advanced stages, but <= 1 year for all BC patients at stage I. The proportion of already cured prevalent BC women was 75% (94% at stage I). Prevalent CRC cases were 422,407 (0.7% of the Italian population), 90% will not die of CRC. For CRC patients, CF was 56%, 92% at stage I, 71% at stage II, and 35% at stages III-IV. TTC was <= 10 years for all age groups and stages. Already cured were 59% of all prevalent CRC patients (93% at stage I). Cancer cure indicators by stage may contribute to appropriate follow-up in the years after diagnosis, thus avoiding patients' discrimination

Causes of death in women with breast cancer: a risks and rates study on a population-based cohort

IntroductionThe increasing survival of patients with breast cancer has prompted the assessment of mortality due to all causes of death in these patients. We estimated the absolute risks of death from different causes, useful for health-care planning and clinical prediction, as well as cause-specific hazards, useful for hypothesis generation on etiology and risk factors.Materials and methodsUsing data from population-based cancer registries we performed a retrospective study on a cohort of women diagnosed with primary breast cancer. We carried out a competing-cause analysis computing cumulative incidence functions (CIFs) and cause-specific hazards (CSHs) in the whole cohort, separately by age, stage and registry area.ResultsThe study cohort comprised 12,742 women followed up for six years. Breast cancer showed the highest CIF, 13.71%, and cardiovascular disease was the second leading cause of death with a CIF of 3.60%. The contribution of breast cancer deaths to the CIF for all causes varied widely by age class: 89.25% in women diagnosed at age <50 years, 72.94% in women diagnosed at age 50–69 and 48.25% in women diagnosed at age ≥70. Greater CIF variations were observed according to stage: the contribution of causes other than breast cancer to CIF for all causes was 73.4% in women with stage I disease, 42.9% in stage II–III and only 13.2% in stage IV. CSH computation revealed temporal variations: in women diagnosed at age ≥70 the CSH for breast cancer was equaled by that for cardiovascular disease and “other diseases” in the sixth year following diagnosis, and an early peak for breast cancer was identified in the first year following diagnosis. Among women aged 50–69 we identified an early peak for breast cancer followed by a further peak near the second year of follow-up. Comparison by geographic area highlighted conspicuous variations: the highest CIF for cardiovascular disease was more than 70% higher than the lowest, while for breast cancer the highest CIF doubled the lowest.ConclusionThe integrated interpretation of absolute risks and hazards suggests the need for multidisciplinary surveillance and prevention using community-based, holistic and well-coordinated survivorship care models

Distant metastasis time to event analysis with CNNs in independent head and neck cancer cohorts

Deep learning models based on medical images play an increasingly important role for cancer outcome prediction. The standard approach involves usage of convolutional neural networks (CNNs) to automatically extract relevant features from the patient's image and perform a binary classification of the occurrence of a given clinical endpoint. In this work, a 2D-CNN and a 3D-CNN for the binary classification of distant metastasis (DM) occurrence in head and neck cancer patients were extended to perform time-to-event analysis. The newly built CNNs incorporate censoring information and output DM-free probability curves as a function of time for every patient. In total, 1037 patients were used to build and assess the performance of the time-to-event model. Training and validation was based on 294 patients also used in a previous benchmark classification study while for testing 743 patients from three independent cohorts were used. The best network could reproduce the good results from 3-fold cross validation Harrell's concordance indices (HCIs) of 0.78, 0.74 and 0.80 in two out of three testing cohorts (HCIs of 0.88, 0.67 and 0.77). Additionally, the capability of the models for patient stratification into high and low-risk groups was investigated, the CNNs being able to significantly stratify all three testing cohorts. Results suggest that image-based deep learning models show good reliability for DM time-to-event analysis and could be used for treatment personalisation

Nasopharyngeal cancer: the impact of guidelines and teaching on radiation target volume delineation

Target volume delineation in the radiation treatment of nasopharyngeal cancer is challenging due to several reasons such as the complex anatomy of the site, the need for the elective coverage of definite anatomical regions, the curative intent of treatment and the rarity of the disease, especially in non-endemic areas. We aimed to analyze the impact of educational interactive teaching courses on target volume delineation accuracy between Italian radiation oncology centers. Only one contour dataset per center was admitted. The educational course consisted in three parts: (1) The completely anonymized image dataset of a T4N1 nasopharyngeal cancer patient was shared between centers before the course with the request of target volume and organs at risk delineation; (2) the course was held online with dedicated multidisciplinary sessions on nasopharyngeal anatomy, nasopharyngeal cancer pattern of diffusion and on the description and explanation of international contouring guidelines. At the end of the course, the participating centers were asked to resubmit the contours with appropriate corrections; (3) the pre- and post-course contours were analyzed and quantitatively and qualitatively compared with the benchmark contours delineated by the panel of experts. The analysis of the 19 pre- and post-contours submitted by the participating centers revealed a significant improvement in the Dice similarity index in all the clinical target volumes (CTV1, CTV2 and CTV3) passing from 0.67, 0.51 and 0.48 to 0.69, 0.65 and 0.52, respectively. The organs at risk delineation was also improved. The qualitative analysis consisted in the evaluation of the inclusion of the proper anatomical regions in the target volumes; it was conducted following internationally validated guidelines of contouring for nasopharyngeal radiation treatment. All the sites were properly included in target volume delineation by >50% of the centers after correction. A significant improvement was registered for the skull base, the sphenoid sinus and the nodal levels. These results demonstrated the important role that educational courses with interactive sessions could have in such a challenging task as target volume delineation in modern radiation oncology

Rising Trend in the Prevalence of HPV-Driven Oropharyngeal Squamous Cell Carcinoma during 2000–2022 in Northeastern Italy: Implication for Using p16^INK4a as a Surrogate Marker for HPV-Driven Carcinogenesis

Background: The prevalence and incidence of oropharyngeal squamous cell carcinomas (OPSCCs) driven by human papillomavirus (HPV) infection are increasing worldwide, being higher in high-income countries. However, data from Italy are scanty. p16INK4a overexpression is the standard in determining HPV-driven carcinogenesis, but disease prevalence impacts on its positive predictive value. Methods: This is a multicenter retrospective study enrolling 390 consecutive patients aged ≥18 years, diagnosed with pathologically confirmed OPSCC in Northeastern Italy between 2000 and 2022. High-risk HPV-DNA and p16INK4a status were retrieved from medical records or evaluated in formalin-fixed paraffin-embedded specimens. A tumor was defined as HPV-driven when double positive for high-risk HPV-DNA and p16INK4a overexpression. Results: Overall, 125 cases (32%) were HPV-driven, with a significant upward temporal trend from 12% in 2000–2006 to 50% in 2019–2022. The prevalence of HPV-driven cancer of the tonsil and base of the tongue increased up to 59%, whereas it remained below 10% in other subsites. Consequently, the p16INK4a positive predictive value was 89% for the former and 29% for the latter. Conclusions: The prevalence of HPV-driven OPSCC continued to increase, even in the most recent period. When using p16INK4a overexpression as a surrogate marker of transforming HPV infection, each institution should consider the subsite-specific prevalence rates of HPV-driven OPSCC as these significantly impact on its positive predictive value