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There are longstanding concerns relating to clinical academic training pipelines, with evidence for multiple barriers and enablers to clinical academic career progression. We sought to assess the extent to which these and other factors apply to academic training in clinical oncology in the United Kingdom. A cross-sectional survey was undertaken using a bespoke, pre-piloted online electronic questionnaire that was distributed to clinical oncology specialty trainees and consultants who had at any point between January 2013–January 2024 commenced an academic post whilst in training. Collated information included demographic data, location and stage of training, research experience and ambitions, research skill confidence and academic career progression. Seventy eligible responses were included, representing 84% (n = 16/19) of UK training deaneries. Thirty-seven (53%) of the respondents had obtained their certificate of completion of training (CCT) whilst 11% (n = 8/70) and 40% (n = 28/70) were at specialty trainee level and respectively pre- or within-/post-doctoral studies. Of 34 post-CCT respondents, 58% (n = 20) had ongoing research commitments but this reached 30% of their overall activity for just 30% (n = 10). Barriers to academic progression included clinical training requirements, post availability and limited mentorship. Most (60%; n = 35/58) undertook doctoral studies in their final two training years. A majority of respondents lacked confidence in radiation oncology (RO) skills relevant to their career ambitions, with 60%, 40% and 30%, respectively, confident in RO clinical research outcome evaluation, in vitro radiation analyses and using RO animal models. These data provide a granular, long-term analysis of academic clinical oncology training at a national level; identifying poor progression to research independence underlined by limited confidence in RO research skills and multiple barriers to academic career progression. These data provide areas in which policy makers, research funders and training programmes can focus to improve academic training in clinical oncology. • Clinician scientists are vital to the continued development of radiation oncology. • This work explores viewpoints on clinical oncology clinical academic training. • There is poor progression to senior clinician scientist roles. • Clinical training, postgraduate exams and limited funding impact progression. • Even senior clinical academics lack confidence in key research skills. [ABSTRACT FROM AUTHOR]

Purpose: Large language models (LLM) show potential for decision support in breast cancer care. Their use in clinical care is currently prohibited by lack of control over sources used for decision-making, explainability of the decision-making process and health data security issues. Recent development of Small Language Models (SLM) is discussed to address these challenges. This preclinical proof-of-concept study tailors an open-source SLM to the German breast cancer guideline (BC-SLM) to evaluate initial clinical accuracy and technical functionality in a preclinical simulation. Methods: A multidisciplinary tumor board (MTB) is used as the gold-standard to assess the initial clinical accuracy in terms of concordance of the BC-SLM with MTB and comparing it to two publicly available LLM, ChatGPT3.5 and 4. The study includes 20 fictional patient profiles and recommendations for 5 treatment modalities, resulting in 100 binary treatment recommendations (recommended or not recommended). Statistical evaluation includes concordance with MTB in % including Cohen’s Kappa statistic (κ). Technical functionality is assessed qualitatively in terms of local hosting, adherence to the guideline and information retrieval. Results: The overall concordance amounts to 86% for BC-SLM (κ = 0.721, p < 0.001), 90% for ChatGPT4 (κ = 0.820, p < 0.001) and 83% for ChatGPT3.5 (κ = 0.661, p < 0.001). Specific concordance for each treatment modality ranges from 65 to 100% for BC-SLM, 85–100% for ChatGPT4, and 55–95% for ChatGPT3.5. The BC-SLM is locally functional, adheres to the standards of the German breast cancer guideline and provides referenced sections for its decision-making. Conclusion: The tailored BC-SLM shows initial clinical accuracy and technical functionality, with concordance to the MTB that is comparable to publicly-available LLMs like ChatGPT4 and 3.5. This serves as a proof-of-concept for adapting a SLM to an oncological disease and its guideline to address prevailing issues with LLM by ensuring decision transparency, explainability, source control, and data security, which represents a necessary step towards clinical validation and safe use of language models in clinical oncology. [ABSTRACT FROM AUTHOR]

Simple Summary: This research is the first comprehensive study in New Zealand to categorise and examine the characteristics of breast cancer based on HER2 status. We explored three HER2 categories: HER2-zero, HER2-low, and HER2-positive, in women diagnosed with invasive breast cancer. Utilising Te Rēhita Mate Ūtaetae (Breast Cancer Foundation NZ National Register), our study analysed data spanning 21 years, revealing that most women underwent HER2 testing. Significantly, many cases previously not recognised as having significant HER2 levels were in fact HER2-low, qualifying them for newer, targeted drug therapies. These findings are particularly crucial as they suggest that newer therapies could benefit a larger segment of patients, notably those with advanced breast cancer; approximately 60% of these women might now benefit from these innovative HER2-targeted treatments. The study underscores the urgent need for standardised HER2 testing to personalise and optimise treatment, enhancing outcomes for patients with invasive breast cancer. Objectives: To perform the first national analysis of demographic and clinicopathological features associated with the HER2 positive, HER2-low, and HER2-zero invasive breast cancers in New Zealand. The study will reveal the proportion of women who may benefit from new HER2-targeted antibody drug conjugate (ADC) therapies. Methods: Utilising data from Te Rēhita Mate Ūtaetae (Breast Cancer Foundation NZ National Register), the study analysed data from women diagnosed with invasive breast cancer over a 21-year period. The HER2 status of tumours was classified into three categories—HER2-zero, HER2-low, HER2-positive. Results: From 2009–2021, 94% of women underwent HER2 testing, with 14% diagnosed with HER2-positive breast cancer. For advanced-stage disease, 38% of those formerly classified as HER2-negative were reclassified as HER2-low. Including HER2-positive breast cancers, this indicates that 60% of women with advanced breast cancer may potentially benefit from the new HER2-directed ADCs (approximately 120 women per year). Conclusions: The findings suggest a significant proportion of women with invasive breast cancer in New Zealand could benefit from new HER2-targeted treatments. There is a need to standardise HER2 testing to enhance personalised treatment and improve outcomes. [ABSTRACT FROM AUTHOR]

Aim: Next-generation sequencing (NGS) of solid tumors can inform treatment decisions; however, uptake remains low. This objective of this systematic review was to identify barriers to and facilitators of NGS in US oncology settings. Materials & methods: Embase and MEDLINE were searched in March 2023 for articles published from 2012 to 2023 on barriers and facilitators of NGS adoption for solid tumors. Surveys, interviews and observational studies were eligible. Studies on genetic testing for hereditary cancers and non-US studies were excluded. The Motheral scale, Joanna Briggs Institute critical appraisal checklist and McGill Mixed Methods Appraisal Tool were used to assess study quality. Data were synthesized narratively. Results: Twenty-one studies were included. Study participants were clinicians, payers and administrators. Key barriers included complex reimbursement processes and uncertainties around clinical utility. Including recommendations for NGS in clinical practice guidelines was a key facilitator, although insurance policies were often more restrictive than guideline recommendations. Conclusion: Uptake of NGS is increasing but barriers remain. Changes to the current reimbursement frameworks are needed to increase access to NGS. The impact of implementing the 2018 National Coverage Determination, which allows access to NGS for all Medicare beneficiaries with advanced cancer, is not yet evident in the published literature. Article highlights Next-generation sequencing (NGS) is used in oncology to detect gene mutations such as NTRK, ALK, MET, ROS1 and ALK in solid tumors and thereby inform treatment decisions. Actionable mutations identified by NGS can be treated with tumor-agnostic targeted therapies, such as larotrectinib and entrectinib. We conducted a systematic review to investigate the reasons underlying the documented under-use of NGS testing for predictive biomarkers in oncology in the United States. The 21 studies included in the review represent viewpoints from clinicians, lab personnel, commercial payers and patients with advanced cancer. Barriers to NGS testing included complex reimbursement processes, uncertainties around clinical utility, lack of staff training and knowledge and inadequate infrastructure. Facilitators were reported less frequently than barriers, but decision support tools for clinicians and the inclusion of NGS testing in clinical practice guidelines appear to aid the uptake of NGS testing. Despite clinical guidelines' preference for NGS testing, commercial insurers often do not offer coverage that is commensurate with the guidelines. Opportunities for increased adoption of NGS testing should be further explored by payers, clinicians and healthcare system administrators by, for example, revising the current payer framework and investing in staff resources and training. Future studies should investigate the impact of Medicare's National Coverage Determination for NGS testing, which came into effect in 2018 and covers NGS for patients with advanced cancer. [ABSTRACT FROM AUTHOR]

The segmentation of head and neck (H&N) tumors in dual Positron Emission Tomography/Computed Tomography (PET/CT) imaging is a critical task in medical imaging, providing essential information for diagnosis, treatment planning, and outcome prediction. Motivated by the need for more accurate and robust segmentation methods, this study addresses key research gaps in the application of deep learning techniques to multimodal medical images. Specifically, it investigates the limitations of existing 2D and 3D models in capturing complex tumor structures and proposes an innovative 2.5D UNet Transformer model as a solution. The primary research questions guiding this study are: (1) How can the integration of convolutional neural networks (CNNs) and transformer networks enhance segmentation accuracy in dual PET/CT imaging? (2) What are the comparative advantages of 2D, 2.5D, and 3D model configurations in this context? To answer these questions, we aimed to develop and evaluate advanced deep-learning models that leverage the strengths of both CNNs and transformers. Our proposed methodology involved a comprehensive preprocessing pipeline, including normalization, contrast enhancement, and resampling, followed by segmentation using 2D, 2.5D, and 3D UNet Transformer models. The models were trained and tested on three diverse datasets: HeckTor2022, AutoPET2023, and SegRap2023. Performance was assessed using metrics such as Dice Similarity Coefficient, Jaccard Index, Average Surface Distance (ASD), and Relative Absolute Volume Difference (RAVD). The findings demonstrate that the 2.5D UNet Transformer model consistently outperformed the 2D and 3D models across most metrics, achieving the highest Dice and Jaccard values, indicating superior segmentation accuracy. For instance, on the HeckTor2022 dataset, the 2.5D model achieved a Dice score of 81.777 and a Jaccard index of 0.705, surpassing other model configurations. The 3D model showed strong boundary delineation performance but exhibited variability across datasets, while the 2D model, although effective, generally underperformed compared to its 2.5D and 3D counterparts. Compared to related literature, our study confirms the advantages of incorporating additional spatial context, as seen in the improved performance of the 2.5D model. This research fills a significant gap by providing a detailed comparative analysis of different model dimensions and their impact on H&N segmentation accuracy in dual PET/CT imaging. [ABSTRACT FROM AUTHOR]

Abstract Background With the development of immune checkpoint inhibitors for the treatment of non-small cell lung cancer, the need for new functional imaging techniques and early response assessments has increased to account for new response patterns and the high cost of treatment. The present study was designed to assess the prognostic impact of dynamic contrast-enhanced computed tomography (DCE-CT) on survival outcomes in non-small cell lung cancer patients treated with immune checkpoint inhibitors. Methods Thirty-three patients with inoperable non-small-cell lung cancer treated with immune checkpoint inhibitors were prospectively enrolled for DCE-CT as part of their follow-up. A single target lesion at baseline and subsequent follow-up examinations were enclosed in the DCE-CT. Blood volume deconvolution (BVdecon), blood flow deconvolution (BFdecon), blood flow maximum slope (BFMax slope) and permeability were assessed using overall survival (OS) and progression-free survival (PFS) as endpoints in Kaplan Meier and Cox regression analyses. Results High baseline Blood Volume (BVdecon) (> 12.97 ml × 100 g−1) was associated with a favorable OS (26.7 vs 7.9 months; p = 0.050) and PFS (14.6 vs 2.5 months; p = 0.050). At early follow-up on day seven a higher relative increase in BFdecon (> 24.50% for OS and > 12.04% for PFS) was associated with an unfavorable OS (8.7 months vs 23.1 months; p

Artificial intelligence (AI) promises to be the next revolutionary step in modern society. Yet, its role in all fields of industry and science need to be determined. One very promising field is represented by AI‐based decision‐making tools in clinical oncology leading to more comprehensive, personalized therapy approaches. In this review, the authors provide an overview on all relevant technical applications of AI in oncology, which are required to understand the future challenges and realistic perspectives for decision‐making tools. In recent years, various applications of AI in medicine have been developed focusing on the analysis of radiological and pathological images. AI applications encompass large amounts of complex data supporting clinical decision‐making and reducing errors by objectively quantifying all aspects of the data collected. In clinical oncology, almost all patients receive a treatment recommendation in a multidisciplinary cancer conference at the beginning and during their treatment periods. These highly complex decisions are based on a large amount of information (of the patients and of the various treatment options), which need to be analyzed and correctly classified in a short time. In this review, the authors describe the technical and medical requirements of AI to address these scientific challenges in a multidisciplinary manner. Major challenges in the use of AI in oncology and decision‐making tools are data security, data representation, and explainability of AI‐based outcome predictions, in particular for decision‐making processes in multidisciplinary cancer conferences. Finally, limitations and potential solutions are described and compared for current and future research attempts. [ABSTRACT FROM AUTHOR]

Differential attainment (DA) in post graduate medical training is a recognised challenge and refers to unexplained variation across groups when split by several protected characteristics. The Royal College of Radiology is committed to fostering diversity, inclusivity, and equality with the goal of narrowing existing gaps and improving training outcomes. This was a mixed methods study aiming to understand the causes of DA with view to helping the RCR develop strategies to address this. A cross-sectional survey was completed by 140 clinical oncology trainees in September 2022. Trainees and trainers (17 and 6 respectively) from across England, Scotland, Wales and Northern Ireland, took part in focus group and interviews from August to December 2022. Quantitative and qualitative data merged and interpreted. Results showed international medical graduates and trainees from ethnic minority backgrounds were more likely to encounter challenges. The qualitative findings were used to identify three themes through which these problems could be framed. The trainee as a "space invader," the hidden curriculum of clinical oncology training and the process of navigating and tacking the training journey. Differential attainment is the product of a complex interplay between the trainee, trainer, and the training environment. Therefore, interventions must be tailored to different people and contexts. At a national level, the RCR can adopt general policies to promote this such as mentorship programmes, protected time for supervision and cultural competency training. Efficacy of proposed interventions for trial and their impact on DA should be evaluated to drive evidence-based changes. • The GMC and Royal Colleges are committed to bridging existing gaps and addressing differential attainment. • A mixed-method research approach to provide a holistic understanding of differential attainment in Clinical Oncology. • Analysis of exclusionary mechanisms identified three main themes: space invaders, hidden curriculum and navigating. [ABSTRACT FROM AUTHOR]

Abstract Background Positron emission tomography (PET) is a highly sensitive method that provides fine resolution images, useful in the field of clinical diagnostics. In this context, Zirconium-89 (89Zr)-based imaging agents have represented a great challenge in molecular imaging with immuno-PET, which employs antibodies (mAbs) as biological vectors. Indeed, immuno-PET requires radionuclides that can be attached to the mAb to provide stable in vivo conjugates, and for this purpose, the radioactive element should have a decay half-life compatible with the time needed for the biodistribution of the immunoglobulin. In this regard, 89Zr is an ideal radioisotope for immuno-PET because its half-life perfectly matches the in vivo pharmacokinetics of mAbs. Results The main objective of this work was the design and synthesis of a series of bifunctional octadentate pseudopeptides able to generate stable 89Zr complexes. To achieve this, here we investigated hydroxamate, N-methylhydroxamate and catecholate chelating moieties in complexing radioactive zirconium. N-methylhydroxamate proved to be the most effective 89Zr-chelating group. Furthermore, the increased flexibility and hydrophilicity obtained by using polyoxyethylene groups spacing the hydroxamate units led to chelators capable of rapidly forming (15 min) stable and water-soluble complexes with 89Zr under mild reaction conditions (aqueous environment, room temperature, and physiological pH) that are mandatory for complexation reactions involving biomolecules. Additionally, we report challenge experiments with the competitor ligand EDTA and metal ions such as Fe3+, Zn2+ and Cu2+. In all examined conditions, the chelators demonstrated stability against transmetallation. Finally, a maleimide moiety was introduced to apply one of the most promising ligands in bioconjugation reactions through Thiol-Michael chemistry. Conclusion Combining solid phase and solution synthesis techniques, we identified novel 89Zr-chelating molecules with a peptide scaffold. The adopted chemical design allowed modulation of molecular flexibility, hydrophilicity, as well as the decoration with different zirconium chelating groups. Best results in terms of 89Zr-chelating properties were achieved with the N-methyl hydroxamate moiety. The Zirconium complexes obtained with the most effective compounds were water-soluble, stable to transmetallation, and resistant to peptidases for at least 6 days. Further studies are needed to assess the potential of this novel class of molecules as Zirconium-chelating agents for in vivo applications.

Abstract Background There is no updated national data regarding the real impact of the COVID-19 pandemic on delaying diagnosis and treatment among patients with lung, and head, and neck cancers in Brazil. This study aimed to analyze the COVID-19 pandemic impact on cancer diagnosis and clinical outcomes among lung, head, and neck cancer patients assisted in a tertiary cancer center in Southeastern Brazil, as well as to analyze these patients’ pretreatment clinical features. Methods Retrospective cohort of patients with lung or head and neck cancer assisted in a tertiary cancer center in southeastern Brazil between January/2019 and December/2021. To assess statistical differences among groups [i.e., cohort 2019 versus (vs.) 2020 and 2019 vs. 2021] chi-square test was used with a 5% significance level and 90% power for sample size calculation. Differences among baseline clinical features and sociodemographic characteristics were evaluated either by T-test for two samples or Fisher’s or Pearson’s chi-square test (for quantitative or qualitative variables). All utilized tests had a 5% significance level. Results Six hundred fifty-two patients were included, 332 with lung and 320 with head and neck cancer; it was observed a significant decrease in oncologic treatment recommendations and increase in palliative care recommendation for patients with lung cancer, despite similar stages at diagnosis. During the COVID-19 pandemic, more pain symptoms were reported at the first Oncology assessment for patients with head and neck cancer (p

Objective: In this study, we aimed to evaluate the effects of demographic data and comorbid diseases on the prognosis and treatment diagnosed with laryngeal squamous cell cancer (LSCC). Methods: Medical records of LSCC patients treated and followed up in a single referral center between 2008 and 2019 were retrospectively reviewed. In addition to the demographic data, the results of overall survival (OS), disease-specific survival (DSS), disease-free survival (DFS), locoregional-free survival (LRFS), and factors affecting recurrence were analyzed. Results: We included 573 patients with a mean age of 60.1±9.8 years. Of the 573 patients, 94.2% (540) were men, 93.7% (537) were smokers, 40.1% had at least one comorbid disease, and 69.8% (400) presented with glottic LSCC. The five-year OS, DSS, DFS, and LRFS rates for all cases were 65.7%, 79.9%, 67%, and 74.7%, respectively. In early-stage LSCC treatment, the rates of OS (p=0.008), DFS (p=0.024) and LRFS (p=0.01) were statistically significantly higher in the endolaryngeal laser surgery (ELS) group compared with the radiotherapy (RT) group. In advanced-stage LSCC treatment, total laryngectomy had statistically significantly higher five-year DFS (p=0.003) and LRFS (p=0.002) rates compared to chemoradiotherapy. Conclusion: Our study showed that ELS provided higher rates of OS, DFS, and LRFS compared to RT in the treatment of early-stage LSCC. Recurrence was significantly higher in supraglottic tumors, advanced-stage tumors, and in patients with clinical N positivity.

Heart rate variability (HRV) analysis provides an assessment of cardiac vagal tone and consequently global cardiac health as well as systemic condition. In systemic diseases such as cancer and during treatments that affect the whole body, like chemotherapy, the vagus nerve activity is low and deregulated. Some studies focus on using HRV to predict mortality in oncology. However, in cancer patients, systemic alterations substantially increase artifacts during HRV measurement, especially atrial ectopic beats. Moreover, HRV may be altered by various factors (duration and time of measurement, breathing, drugs, and other confounding factors) that alter each metric in different ways. The Standard Deviation of all Normal to Normal intervals (SDNN) is the most commonly used metric to evaluate HRV in oncology, but it does not appear to be specific to the cardiac vagal tone. Thus, cardiac vagal activity diagnosis and vital prognosis of cancer patients can be biased. Our review presents the main HRV metrics that can be currently used in oncology studies and their links with vagus nerve and cancer. We present the influence of external factors and the required duration and time of measurement. Considering all these parameters, this review proposes seven key points for an assessment of HRV and cardiac vagal tone in patients with cancer.

Abstract Background Botulinum toxin, produced by the Gram-positive anaerobe Clostridium botulinum, is composed of seven antigenic subtypes (A, B, C, D, E, F, and G). Currently, only Botulinum toxin type A, commonly referred to as “Botox,” is approved for clinical use, given its relatively safe clinical profile. Botulinum toxin type A has a wide range of therapeutic indications, including treatment for dystonia, migraine headache, neurogenic bladder, and large muscle spastic disorders. However, the toxin is most widely known for its cosmetic effects in treating wrinkles and facial lines. Case presentation This article describes a 62-year-old Caucasian female who presented for investigation and workup of an isolated lytic lesion of her frontal bone a few weeks after administration of botulinum toxin injection into the corresponding site in the frontalis muscle. This presented as a large, palpable, painless forehead lump causing significant psychological distress. After no neoplastic cause for the lesion was found and histopathology was performed, our researchers concluded that the most likely explanation was that the bony lytic lesion resulted from inadvertent injection of the “Botox” neurotoxin through the intended target muscle and into the cortex of the underlying bone. Conclusions Our search of the literature failed to identify any previous cases of this occurring. However, as the popularity of this cosmetic procedure only increases, we believe that this represents an important possible differential for isolated lytic lesion after administration of “Botox” injection.

Background: Positron emission tomography (PET) is a highly sensitive method that provides fine resolution images, useful in the field of clinical diagnostics. In this context, Zirconium-89 (89Zr)-based imaging agents have represented a great challenge in molecular imaging with immuno-PET, which employs antibodies (mAbs) as biological vectors. Indeed, immuno-PET requires radionuclides that can be attached to the mAb to provide stable in vivo conjugates, and for this purpose, the radioactive element should have a decay half-life compatible with the time needed for the biodistribution of the immunoglobulin. In this regard, 89Zr is an ideal radioisotope for immuno-PET because its half-life perfectly matches the in vivo pharmacokinetics of mAbs. Results: The main objective of this work was the design and synthesis of a series of bifunctional octadentate pseudopeptides able to generate stable 89Zr complexes. To achieve this, here we investigated hydroxamate, N-methylhydroxamate and catecholate chelating moieties in complexing radioactive zirconium. N-methylhydroxamate proved to be the most effective 89Zr-chelating group. Furthermore, the increased flexibility and hydrophilicity obtained by using polyoxyethylene groups spacing the hydroxamate units led to chelators capable of rapidly forming (15 min) stable and water-soluble complexes with 89Zr under mild reaction conditions (aqueous environment, room temperature, and physiological pH) that are mandatory for complexation reactions involving biomolecules. Additionally, we report challenge experiments with the competitor ligand EDTA and metal ions such as Fe3+, Zn2+ and Cu2+. In all examined conditions, the chelators demonstrated stability against transmetallation. Finally, a maleimide moiety was introduced to apply one of the most promising ligands in bioconjugation reactions through Thiol-Michael chemistry. Conclusion: Combining solid phase and solution synthesis techniques, we identified novel 89Zr-chelating molecules with a peptide scaffold. The adopted chemical design allowed modulation of molecular flexibility, hydrophilicity, as well as the decoration with different zirconium chelating groups. Best results in terms of 89Zr-chelating properties were achieved with the N-methyl hydroxamate moiety. The Zirconium complexes obtained with the most effective compounds were water-soluble, stable to transmetallation, and resistant to peptidases for at least 6 days. Further studies are needed to assess the potential of this novel class of molecules as Zirconium-chelating agents for in vivo applications. [ABSTRACT FROM AUTHOR]

Background: There is no updated national data regarding the real impact of the COVID-19 pandemic on delaying diagnosis and treatment among patients with lung, and head, and neck cancers in Brazil. This study aimed to analyze the COVID-19 pandemic impact on cancer diagnosis and clinical outcomes among lung, head, and neck cancer patients assisted in a tertiary cancer center in Southeastern Brazil, as well as to analyze these patients' pretreatment clinical features. Methods: Retrospective cohort of patients with lung or head and neck cancer assisted in a tertiary cancer center in southeastern Brazil between January/2019 and December/2021. To assess statistical differences among groups [i.e., cohort 2019 versus (vs.) 2020 and 2019 vs. 2021] chi-square test was used with a 5% significance level and 90% power for sample size calculation. Differences among baseline clinical features and sociodemographic characteristics were evaluated either by T-test for two samples or Fisher's or Pearson's chi-square test (for quantitative or qualitative variables). All utilized tests had a 5% significance level. Results: Six hundred fifty-two patients were included, 332 with lung and 320 with head and neck cancer; it was observed a significant decrease in oncologic treatment recommendations and increase in palliative care recommendation for patients with lung cancer, despite similar stages at diagnosis. During the COVID-19 pandemic, more pain symptoms were reported at the first Oncology assessment for patients with head and neck cancer (p < 0.05). Compared to 2019, head and neck cancer patients diagnosed in 2021 presented a worse initial performance status (p = 0.008). There was a statistically significant increase in survival for patients diagnosed with head and neck cancer in 2021 when compared to 2019 (p = 0.003). Conclusions: This research highlights low survival rates for patients with lung and head and neck cancer in Brazil, even before the pandemic started, as consequence of advanced diseases at diagnosis at the public health system and clinical degrading features. Additionally, there was an increase incidence in both lung cancer and head and neck cancer despite no differences in clinical stage. This reflects how fragile is the public healthcare system even before facing an acute public health crisis such as the COVID-19 pandemic. Yet, the total impact on public health may follow for many years. [ABSTRACT FROM AUTHOR]

Background: Botulinum toxin, produced by the Gram-positive anaerobe Clostridium botulinum, is composed of seven antigenic subtypes (A, B, C, D, E, F, and G). Currently, only Botulinum toxin type A, commonly referred to as "Botox," is approved for clinical use, given its relatively safe clinical profile. Botulinum toxin type A has a wide range of therapeutic indications, including treatment for dystonia, migraine headache, neurogenic bladder, and large muscle spastic disorders. However, the toxin is most widely known for its cosmetic effects in treating wrinkles and facial lines. Case presentation: This article describes a 62-year-old Caucasian female who presented for investigation and workup of an isolated lytic lesion of her frontal bone a few weeks after administration of botulinum toxin injection into the corresponding site in the frontalis muscle. This presented as a large, palpable, painless forehead lump causing significant psychological distress. After no neoplastic cause for the lesion was found and histopathology was performed, our researchers concluded that the most likely explanation was that the bony lytic lesion resulted from inadvertent injection of the "Botox" neurotoxin through the intended target muscle and into the cortex of the underlying bone. Conclusions: Our search of the literature failed to identify any previous cases of this occurring. However, as the popularity of this cosmetic procedure only increases, we believe that this represents an important possible differential for isolated lytic lesion after administration of "Botox" injection. [ABSTRACT FROM AUTHOR]

Objective: In this study, we aimed to evaluate the effects of demographic data and comorbid diseases on the prognosis and treatment diagnosed with laryngeal squamous cell cancer (LSCC). Methods: Medical records of LSCC patients treated and followed up in a single referral center between 2008 and 2019 were retrospectively reviewed. In addition to the demographic data, the results of overall survival (OS), disease-specific survival (DSS), disease-free survival (DFS), locoregional-free survival (LRFS), and factors affecting recurrence were analyzed. Results: We included 573 patients with a mean age of 60.1±9.8 years. Of the 573 patients, 94.2% (540) were men, 93.7% (537) were smokers, 40.1% had at least one comorbid disease, and 69.8% (400) presented with glottic LSCC. The five-year OS, DSS, DFS, and LRFS rates for all cases were 65.7%, 79.9%, 67%, and 74.7%, respectively. In early-stage LSCC treatment, the rates of OS (p=0.008), DFS (p=0.024) and LRFS (p=0.01) were statistically significantly higher in the endolaryngeal laser surgery (ELS) group compared with the radiotherapy (RT) group. In advanced-stage LSCC treatment, total laryngectomy had statistically significantly higher five-year DFS (p=0.003) and LRFS (p=0.002) rates compared to chemoradiotherapy. Conclusion: Our study showed that ELS provided higher rates of OS, DFS, and LRFS compared to RT in the treatment of early-stage LSCC. Recurrence was significantly higher in supraglottic tumors, advanced-stage tumors, and in patients with clinical N positivity. [ABSTRACT FROM AUTHOR]

Abstract Artificial intelligence (AI) promises to be the next revolutionary step in modern society. Yet, its role in all fields of industry and science need to be determined. One very promising field is represented by AI‐based decision‐making tools in clinical oncology leading to more comprehensive, personalized therapy approaches. In this review, the authors provide an overview on all relevant technical applications of AI in oncology, which are required to understand the future challenges and realistic perspectives for decision‐making tools. In recent years, various applications of AI in medicine have been developed focusing on the analysis of radiological and pathological images. AI applications encompass large amounts of complex data supporting clinical decision‐making and reducing errors by objectively quantifying all aspects of the data collected. In clinical oncology, almost all patients receive a treatment recommendation in a multidisciplinary cancer conference at the beginning and during their treatment periods. These highly complex decisions are based on a large amount of information (of the patients and of the various treatment options), which need to be analyzed and correctly classified in a short time. In this review, the authors describe the technical and medical requirements of AI to address these scientific challenges in a multidisciplinary manner. Major challenges in the use of AI in oncology and decision‐making tools are data security, data representation, and explainability of AI‐based outcome predictions, in particular for decision‐making processes in multidisciplinary cancer conferences. Finally, limitations and potential solutions are described and compared for current and future research attempts.

Abstract Background During the COVID-19 pandemic, decentralised clinical trials incorporated self-monitoring, self-reporting, and telenursing tools to address health literacy and health empowerment of patients enrolled in clinical trials. We aimed to determine the impact of an educational intervention using telenursing consultations on health literacy, health empowerment, and health-related quality of life in cancer patients enrolled in clinical trials by measuring the level of satisfaction with the care received and assessing the views of healthcare professionals concerning the advanced practice nurse (APN) role in oncology clinical trials. Methods In this pilot analytical, descriptive, longitudinal, quasi-experimental, and pre-post test study, an educational intervention was conducted by 5 visits with an APN using synchronous teleconsultation in patients starting cancer treatment for the first time in a clinical trial (n = 60), and health professionals working with the APN (n = 31). A descriptive analysis of the samples and questionnaires were utilised along with statistical comparisons. Results After the intervention, patients' health literacy (31.7%), health empowerment (18.3%), and health-related quality of life (33.3%) increased (p

The concept of modern palliative care originated in the 1960s. It has been highly valued by the World Health Organization, and promoted globally with the 'three-step analgesic ladder' as the starting point. China introduced the 'three-step analgesic ladder' in 1990, which has been in practice for more than 30 years. This has brought clinical oncology closer to palliative care, thus promoting the progress of palliative care in China. Further development of palliative care in China is expected to significantly improve the survival time and quality of life of cancer patients.

Objective To investigate the needs of eight-year program clinical medical students for the organization and contents of clinical oncology courses. Methods From September to November 2020, a questionnaire survey was conducted among eight-year program clinical medicine students in Peking Union Medical College to find out their knowledge base in oncology, teaching mode preference and course contents of interest. Results A total of 122 students participated in the survey, in which 89.3%(109/122) of the students showed interest in basic and clinical research projects related to oncology, 84.4%(103/122) thought it was better to use Simulation-based medical education (SBME), and 91.0%(111/122) hoped to learn throughoff-line discussion. In terms of course contents, eight-year program medical students were more interested in knowledge directly related to clinical context, such as diagnosis, treatment, multidisciplinary comprehensive treatment and evidence-based medicine. In terms of sub-analysis, traditional eight-year students (86%, 92/107) showed a higher acceptance of palliative care than students in the 4+4 reform program(60%, 9/15) and were more willing to act as scriptwriters in SBME(26% vs. 7%, P=0.013). The students in clinical phase gained a better understanding of oncology knowledge through research training and were more inclined to take on the role of scriptwriters in SBME than those in basic phase (27% vs. 11%, P=0.048). Conclusions The eight-year program clinical medical students are interested in the clinical oncology course and prefer study in the form of Simulation-based medical education (SBME).

Next-generation sequencing (NGS) has taken on major importance in clinical oncology practice. With the advent of targeted therapies capable of effectively targeting specific genomic alterations in cancer patients, the development of bioinformatics processes has become crucial. Thus, bioinformatics pipelines play an essential role not only in the detection and in identification of molecular alterations obtained from NGS data but also in the analysis and interpretation of variants, making it possible to transform raw sequencing data into meaningful and clinically useful information. In this review, we aim to examine the multiple steps of a bioinformatics pipeline as used in current clinical practice, and we also provide an updated list of the necessary bioinformatics tools. This resource is intended to assist researchers and clinicians in their genetic data analyses, improving the precision and efficiency of these processes in clinical research and patient care.

Background: During the COVID-19 pandemic, decentralised clinical trials incorporated self-monitoring, self-reporting, and telenursing tools to address health literacy and health empowerment of patients enrolled in clinical trials. We aimed to determine the impact of an educational intervention using telenursing consultations on health literacy, health empowerment, and health-related quality of life in cancer patients enrolled in clinical trials by measuring the level of satisfaction with the care received and assessing the views of healthcare professionals concerning the advanced practice nurse (APN) role in oncology clinical trials. Methods: In this pilot analytical, descriptive, longitudinal, quasi-experimental, and pre-post test study, an educational intervention was conducted by 5 visits with an APN using synchronous teleconsultation in patients starting cancer treatment for the first time in a clinical trial (n = 60), and health professionals working with the APN (n = 31). A descriptive analysis of the samples and questionnaires were utilised along with statistical comparisons. Results: After the intervention, patients' health literacy (31.7%), health empowerment (18.3%), and health-related quality of life (33.3%) increased (p < 0.05), with a decrease and trend towards resolution of care needs (p < 0.05). Satisfaction with the quality and care received in terms of perceived convenience, transition, and continuity of care showed positive results in 64.9 ± 20.7, 77.6 ± 19.5, and 72.1 ± 20.4 of respondents, respectively. On the overall assessment of the APN role, healthcare professionals expressed a high level of agreement with the statements related to their work performance. Conclusions: The data indicates that a clinical trial APN-led telenursing educational intervention results in an overall increase in health literacy, an improvement in health empowerment and health-related quality of life, and a decrease in care needs of oncology clinical trials patients. Patients stated that they received a high quality of care and health professionals indicated high levels of acceptance with APNs. Based on these results, we suggest that the APN role should gain more recognition in the Spanish healthcare system and their professional competencies should be aligned with those of other countries. [ABSTRACT FROM AUTHOR]

Introduction: Our objective was to investigate the trajectories of anxiety, depression, emotional and social functioning in women with newly diagnosed vulvar cancer from the time of diagnosis to 12 months after treatment. A further aim was to identify risk factors for high levels of anxiety. Material and methods: PROVE (PROspective Vulvar Cancer Evaluation) is a nationwide longitudinal cohort study investigating quality of life in women with newly diagnosed vulvar cancer by the following validated patient‐reported outcome measures at diagnosis, and 3 and 12 months after treatment: The Hospital Anxiety and Depression Scale, the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire C30, and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Vulvar module VU34. Mean scores, changes over time and associations were analyzed by generalized estimated equations and log‐linear regression models, adjusted for possible confounders. Results: Between 2019 and 2021, 105 (69%) women completed the questionnaires at all three time points. At diagnosis, 42% of the women reported elevated anxiety levels, decreasing significantly to 30% during the first 12 months. Insomnia, persisting vulvar symptoms and high information needs were significantly associated with a high level of anxiety (relative risk [RR] 2.1, 95% CI 1.2–3.7 for insomnia; RR 2.8, 95% CI 1.7–4.6 for vulvar symptoms, RR 2.7, 95% CI 1.5–4.9 for information needs). We found a trend towards a higher level of anxiety in younger women (<65 years: RR 1.5, 95% CI 1.0–2.5). Participants reported a low and stable prevalence of depression (14%) and high social functioning throughout the study period. Conclusions: Women with newly diagnosed vulvar cancer report a high level of anxiety at diagnosis. Despite a significant improvement, anxiety remains widely prevalent during the first year of follow‐up. Targeting insomnia, vulvar symptoms and unmet needs may decrease anxiety during surveillance. [ABSTRACT FROM AUTHOR]

This article, published in Frontiers in Oncology, discusses the advances in mass spectrometry-based proteogenomics and its applications in clinical oncology. Proteogenomics is a field that integrates proteomics data with genomics and transcriptomics data to understand the systems biology of tumor subtypes. The article highlights the importance of proteogenomics in identifying clinical biomarkers, developing treatment strategies, and improving treatment response in cancer patients. It also presents several studies that demonstrate the use of mass spectrometry in identifying biomarkers for prostate cancer, analyzing cancer proteomics datasets, detecting M-proteins in plasma cell dyscrasias, measuring lipid species in colorectal adenoma and carcinoma, and identifying disease-related co-expression networks in solid-predominant lung adenocarcinoma. [Extracted from the article]

Purpose: Gold standard chemotherapy dosage is based on body surface area (BSA); however many patients experience dose-limiting toxicities (DLT). We aimed to evaluate the effectiveness of BSA, two-dimensional (2D) and three-dimensional (3D) body composition (BC) measurements derived from Lumbar 3 vertebra (L3) computed tomography (CT) slices, in predicting DLT in colon cancer patients. Methods: 203 patients (60.87 ± 12.42 years; 97 males, 47.8%) receiving adjuvant chemotherapy (Oxaliplatin and/or 5-Fluorouracil) were retrospectively evaluated. An artificial intelligence segmentation model was used to extract 2D and 3D body composition measurements from each patients' single mid-L3 CT slice as well as multiple-L3 CT scans to produce a 3D BC report. DLT was defined as any incidence of dose reduction or discontinuation due to chemotherapy toxicities. A receiver operating characteristic (ROC) analysis was performed on BSA and individual body composition measurements to demonstrate their predictive performance. Results: A total of 120 (59.1%) patients experienced DLT. Age and BSA did not vary significantly between DLT and non-DLT group. Females were significantly more likely to experience DLT (p = 4.9 × 10–3). In all patients, the predictive effectiveness of 2D body composition measurements (females: AUC = 0.50–0.54; males: AUC = 0.50–0.61) was equivalent to that of BSA (females: AUC = 0.49; males: AUC = 0.58). The L3 3D skeletal muscle volume was the most predictive indicator of DLT (AUC of 0.66 in females and 0.64 in males). Conclusion: Compared to BSA and 2D body composition measurements, 3D L3 body composition measurements had greater potential to predict DLT in CRC patients receiving chemotherapy and this was sex dependent. [ABSTRACT FROM AUTHOR]

Background: Survival in cancer patients is associated with a multitude of biological, social, and psychological factors. Although it is well established that all these factors add to overall mortality, it is not well understood how the predictive power of these parameters changes in a comprehensive model and over time. Methods: Patients who attended the authors' outpatient clinic were invited to participate. The authors followed 5180 mixed cancer patients (51.1% female; mean age, 59.1 years [SD = 13.8]) for up to 16 years and analyzed biological (age, sex, cancer site, anemia), psychological (anxiety, depression), and social variables (marital status, education, employment status) potentially predicting overall survival in a Cox proportional hazards model. Results: The median survival time for the entire sample was 4.3 years (95% confidence interval, 4.0–4.7). The overall survival probabilities for 1 and 10 years were 76.8% and 38.0%, respectively. Following an empirical approach, the authors split the time interval into five periods: acute, subacute, short‐term, medium‐term, and long‐term. A complex pattern of variables predicted overall survival differently in the five periods. Biological parameters were important throughout most of the time, social parameters were either time‐independent predictors or tended to be more important in the longer term. Of the psychological parameters, only depression was a significant predictor and lost its predictive power in the long‐term. Conclusions: The findings of this study allow the development of comprehensive patient‐specific models of risk and resilience factors addressing biopsychosocial needs of cancer patients, paving the way for a personalized treatment plan that goes beyond biomedical cancer care. The complex relationship between bio‐psycho‐social factors and survival should not be broken down into bivariate relationships. A holistic care model for cancer patients should be tailored to the needs of the individual patient against the background of their individual risk factor profile over time. [ABSTRACT FROM AUTHOR]

Background: Delays in starting postoperative radiotherapy (PORT) have been established as negative predictors for clinical outcomes in head and neck squamous cell carcinomas (HNSCC). Our study aimed to examine the effect of delays during PORT, and the impact of national holidays in Canada, a publicly funded system, on oncologic outcomes such as Overall Survival (OS) and Local Recurrence (LR). Methods: The provincial cancer registry was queried to obtain demographic, pathologic, and outcomes data from cancer patients treated for all squamous cell carcinomas of the head and neck region treated between January 1, 2007 and November 30, 2019. All extracted information was cross-referenced and supplemented by chart review of patient electronic medical records. Extracted data were analyzed for OS and LR, in the context of Canadian national holidays causing delays during PORT. Results: 1433 patients treated for HNSCCs were identified, of whom 338 were treated curatively with surgery followed by PORT. 68.6% of patients experienced at least one day of interruption during treatments due to holidays. LR was 15.4% and OS was 59.6% at 5 years. Treatment interruptions by holidays were predictive of local recurrence (HR, 2.38; 95% CI 1.17–4.83; p = 0.017). Patients that developed early recurrence prior to PORT had very poor oncologic outcomes. Conclusion: Our findings were consistent with previously published studies in limiting the interval between surgery and PORT. We identified the novel finding of paired holidays as a significant predictor in determining LR, suggesting the importance of modifying RT delivery schedules and timing.

Gliomas are the most prevalent neurological cancer in the USA and care modalities are not able to effectively combat these aggressive malignancies. Identifying new, more effective treatments require a deep understanding of the complex genetic variations and relevant pathway associations behind these cancers. Drawing connections between gene mutations with a responsive genetic target can help drive therapy selections to enhance patient survival. We have performed extensive molecular profiling of the Capicua gene (CIC), a tumor and transcriptional suppressor gene, and its mutation prevalence in reference to MAPK activation within clinical glioma tissue. CIC mutations occur far more frequently in oligodendroglioma (52.1%) than in low-grade astrocytoma or glioblastoma. CIC-associated mutations were observed across all glioma subtypes, and MAPK-associated mutations were most prevalent in CIC wild-type tissue regardless of the glioma subtype. MAPK activation, however, was enhanced in CIC-mutated oligodendroglioma. The totality of our observations reported supports the use of CIC as a relevant genetic marker for MAPK activation. Identification of CIC mutations, or lack thereof, can assist in selecting, implementing, and developing MEK/MAPK-inhibitory trials to improve patient outcomes potentially. [ABSTRACT FROM AUTHOR]

Aim: Accurate evaluation of glomerular filtration rate (GFR) is crucial in Oncology as drug eligibility and dosing depend on estimates of GFR. However, there are no clear guidelines on the optimal method of determining kidney function in patients with cancer. We aimed to summarize the evidence on estimation of kidney function in patients with cancer. Methods: We searched PubMed for literature discussing the performance of GFR estimating equations in patients with malignancy to create a table of the evidence for creatinine‐ and cystatin c‐based equations. We further reviewed novel estimation techniques such as panel eGFR, real‐time measured GFR, and functional magnetic resonance imaging. Results: The commonly used GFR estimating equations were derived from populations of patients without cancer. These equations may be less applicable in Oncology due to severe sarcopenia, inflammation, and other physiologic changes in patients with cancer. The Cockcroft‐Gault equation currently dominates in clinical Oncology despite significant limitations and accumulating evidence for use of the CKD‐EPICr formula. Additional considerations in the practice of Oncology include a recently developed equation (CamGFRv2, also called the Janowitz formula) and the use of cystatin c‐based equations to overcome some of the barriers to accurate GFR estimation based on creatinine alone. Conclusion: Overall, we suggest using the CKD‐EPI equations (either cystatin c or creatinine‐based) among patients with cancer in routine clinical practice and measured GFR for patients at a critical threshold for treatment decisions. [ABSTRACT FROM AUTHOR]

Abstract Introduction Telehealth (TH) utilization in cancer care prior to COVID‐19 was variable. Research highlights disparities in access determined by socioeconomic factors including education, income, race, and age. In response to COVID‐19 and these disparities, we assessed the impact of a personalized digital support structure, the Telehealth Task Force (TTF), to reduce disparities in TH. Methods We performed a retrospective review of cohorts between January 1, 2020 and August 30, 2020: Pre (TH use with basic telephone support), Intervention (TH access with TTF), and Post (TH access after TTF initiation and educational material dissemination). Data collected included successful TH access, health literacy (HL), and Area Deprivation Index, a ranking of neighborhoods by socioeconomic disadvantage (ADI). The data were analyzed in univariate ordinary least squares model and adjacent categories ratio model using statistical software R to understand the relationship between TTF, HL, ADI, and TH access. Results We included 555 patients from January 1, 2020 to August 30, 2020 (90 preintervention, 194 intervention, and 271 postintervention), excluding patients without ADI/HL. TTF support successfully engaged older, racially, and socioeconomically diverse patients in TH; ADI is significantly higher in the postintervention group vs. preintervention (mean difference = 7.66, 95% CI 1.00–4.32, p = 0.024) and more patients had low HL during intervention compared with preintervention (adjacent categories ratio = 0.62, 95% CI 0.41–0.93, p = 0.021). Discussion COVID‐19 created an immediate need for TH. Implementation of the TTF helped close the digital divide, increasing TH access for vulnerable patients. Attention to digital readiness can mitigate disparities in access to care. Future research should explore the implementation of widespread routine digital support initiatives.

The rapid development of artificial intelligence (AI) technologies in the context of the vast amount of collectable data obtained from high-throughput sequencing has led to an unprecedented understanding of cancer and accelerated the advent of a new era of clinical oncology with a tone of precision treatment and personalized medicine. However, the gains achieved by a variety of AI models in clinical oncology practice are far from what one would expect, and in particular, there are still many uncertainties in the selection of clinical treatment options that pose significant challenges to the application of AI in clinical oncology. In this review, we summarize emerging approaches, relevant datasets and open-source software of AI and show how to integrate them to address problems from clinical oncology and cancer research. We focus on the principles and procedures for identifying different antitumor strategies with the assistance of AI, including targeted cancer therapy, conventional cancer therapy, and cancer immunotherapy. In addition, we also highlight the current challenges and directions of AI in clinical oncology translation. Overall, we hope this article will provide researchers and clinicians with a deeper understanding of the role and implications of AI in precision cancer therapy, and help AI move more quickly into accepted cancer guidelines. [ABSTRACT FROM AUTHOR]

Circulating tumor DNA (ctDNA), accurately described by the term liquid profiling (LP), enables real-time assessment of the tumor mutational profile as a minimally invasive test and has therefore rapidly gained traction, particular for the management of cancer patients. By LP, tumor-specific genetic alterations can be determined as part of companion diagnostics to guide selection of appropriate targeted therapeutics. Because LP facilitates longitudinal monitoring of cancer patients, it can be used to detect acquired resistant mechanisms or as a personalized biomarker for earlier detection of disease recurrence, among other applications. However, LP is not yet integrated into routine care to the extent that might be expected. This is due to the lack of harmonization and standardization of preanalytical and analytical workflows, the lack of proper quality controls, limited evidence of its clinical utility, heterogenous study results, the uncertainty of clinicians regarding the value and appropriate indications for LP and its interpretation, and finally, the lack of reimbursement for most LP tests. In this review, the value proposition of LP for cancer patient management and treatment optimization, the current status of implementation in standard care, and the main challenges that need to be overcome are discussed in detail.

Introduction: Telehealth (TH) utilization in cancer care prior to COVID‐19 was variable. Research highlights disparities in access determined by socioeconomic factors including education, income, race, and age. In response to COVID‐19 and these disparities, we assessed the impact of a personalized digital support structure, the Telehealth Task Force (TTF), to reduce disparities in TH. Methods: We performed a retrospective review of cohorts between January 1, 2020 and August 30, 2020: Pre (TH use with basic telephone support), Intervention (TH access with TTF), and Post (TH access after TTF initiation and educational material dissemination). Data collected included successful TH access, health literacy (HL), and Area Deprivation Index, a ranking of neighborhoods by socioeconomic disadvantage (ADI). The data were analyzed in univariate ordinary least squares model and adjacent categories ratio model using statistical software R to understand the relationship between TTF, HL, ADI, and TH access. Results: We included 555 patients from January 1, 2020 to August 30, 2020 (90 preintervention, 194 intervention, and 271 postintervention), excluding patients without ADI/HL. TTF support successfully engaged older, racially, and socioeconomically diverse patients in TH; ADI is significantly higher in the postintervention group vs. preintervention (mean difference = 7.66, 95% CI 1.00–4.32, p = 0.024) and more patients had low HL during intervention compared with preintervention (adjacent categories ratio = 0.62, 95% CI 0.41–0.93, p = 0.021). Discussion: COVID‐19 created an immediate need for TH. Implementation of the TTF helped close the digital divide, increasing TH access for vulnerable patients. Attention to digital readiness can mitigate disparities in access to care. Future research should explore the implementation of widespread routine digital support initiatives. [ABSTRACT FROM AUTHOR]

The number of cancer patients in Nigeria continues to rise; however, global advances in cancer research are making the provision of optimal care possible. Cancer management is globally agreed to be multidisciplinary, with patients now having the right to benefit from progress in systemic cancer therapy and radio-diagnosis by receiving treatment from adequately trained and highly skilled clinical and radiation oncologists. Radiation oncology is part of the three divisions that make up oncology -- medical oncology, surgical oncology and radiation oncology. This discipline in recent times has been developed into Clinical Oncology and although both clinical oncologists and medical oncologists continue to deliver non-surgical cancer treatment, only clinical oncologists are qualified to deliver radiotherapy in the management of cancers. Though clinical oncologists continue to provide quality cancer workforce for the country's increasing number of cancer patients, much is still unknown about this discipline in Nigeria. It is hoped that inspiring radio-oncologists will take note of the information provided by this article as a guide. This paper chronicles the multifarious process involved in training to become a clinical and radiation-oncologist in Nigeria, plus the requirements, as well as pertinent information a budding physician seeking to advance in this highly specialised field requires. [ABSTRACT FROM AUTHOR]

The ecancer 'Choosing Wisely' conference was held for the first time in Latin America in Santa Cruz, Bolivia. The event had more than 150 registered attendees in addition to 22 speakers from different countries and different specialities in the field of oncology, who presented topics on prevention, oncological surgery, clinical oncology and palliative care, in order to demonstrate the current evidence of how to approach a patient in daily clinical practice based on the human resources, materials and drugs available, trying to offer the maximum benefit to the patient based on current scientific evidence. In addition to addressing issues of vital importance in breast cancer, during the 2 days of the event, updated information generated in recent years was presented, the results of which will change clinical practice. All the experts were in favour of developing strategies and methods that help us to properly select treatments to optimise resources and reduce the economic toxicity of the most modern and current treatments. This conference was an event of vital importance because it was the first face-to-face event for ecancer and the physicians after difficult years due to COVID-19. [ABSTRACT FROM AUTHOR]

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High-risk pediatric B-ALL patients experience 5-year negative event rates up to 25%. Although some biomarkers of relapse are utilized in the clinic, their ability to predict outcomes in high-risk patients is limited. Here, we propose a random survival forest (RSF) machine learning model utilizing interpretable genomic inputs to predict relapse/death in high-risk pediatric B-ALL patients. We utilized whole exome sequencing profiles from 156 patients in the TARGET-ALL study (with samples collected at presentation) further stratified into training and test cohorts (109 and 47 patients, respectively). To avoid overfitting and facilitate the interpretation of machine learning results, input genomic variables were engineered using a stepwise approach involving univariable Cox models to select variables directly associated with outcomes, genomic coordinate-based analysis to select mutational hotspots, and correlation analysis to eliminate feature co-linearity. Model training identified 7 genomic regions most predictive of relapse/death-free survival. The test cohort error rate was 12.47%, and a polygenic score based on the sum of the top 7 variables effectively stratified patients into two groups, with significant differences in time to relapse/death (log-rank P = 0.001, hazard ratio = 5.41). Our model outperformed other EFS modeling approaches including an RSF using gold-standard prognostic variables (error rate = 24.35%). Validation in 174 standard-risk patients and 3 patients who failed to respond to induction therapy confirmed that our RSF model and polygenic score were specific to high-risk disease. We propose that our feature selection/engineering approach can increase the clinical interpretability of RSF, and our polygenic score could be utilized for enhance clinical decision-making in high-risk B-ALL.

Purpose: This study proposed and investigated the feasibility of estimating Patlak-derived influx rate constant (Ki) from standardized uptake value (SUV) and/or dynamic PET image series. Methods: Whole-body 18F-FDG dynamic PET images of 19 subjects consisting of 13 frames or passes were employed for training a residual deep learning model with SUV and/or dynamic series as input and Ki-Patlak (slope) images as output. The training and evaluation were performed using a nine-fold cross-validation scheme. Owing to the availability of SUV images acquired 60 min post-injection (20 min total acquisition time), the data sets used for the training of the models were split into two groups: "With SUV" and "Without SUV." For "With SUV" group, the model was first trained using only SUV images and then the passes (starting from pass 13, the last pass, to pass 9) were added to the training of the model (one pass each time). For this group, 6 models were developed with input data consisting of SUV, SUV plus pass 13, SUV plus passes 13 and 12, SUV plus passes 13 to 11, SUV plus passes 13 to 10, and SUV plus passes 13 to 9. For the "Without SUV" group, the same trend was followed, but without using the SUV images (5 models were developed with input data of passes 13 to 9). For model performance evaluation, the mean absolute error (MAE), mean error (ME), mean relative absolute error (MRAE%), relative error (RE%), mean squared error (MSE), root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) were calculated between the predicted Ki-Patlak images by the two groups and the reference Ki-Patlak images generated through Patlak analysis using the whole acquired data sets. For specific evaluation of the method, regions of interest (ROIs) were drawn on representative organs, including the lung, liver, brain, and heart and around the identified malignant lesions. Results: The MRAE%, RE%, PSNR, and SSIM indices across all patients were estimated as 7.45 ± 0.94%, 4.54 ± 2.93%, 46.89 ± 2.93, and 1.00 ± 6.7 × 10−7, respectively, for models predicted using SUV plus passes 13 to 9 as input. The predicted parameters using passes 13 to 11 as input exhibited almost similar results compared to the predicted models using SUV plus passes 13 to 9 as input. Yet, the bias was continuously reduced by adding passes until pass 11, after which the magnitude of error reduction was negligible. Hence, the predicted model with SUV plus passes 13 to 9 had the lowest quantification bias. Lesions invisible in one or both of SUV and Ki-Patlak images appeared similarly through visual inspection in the predicted images with tolerable bias. Conclusion: This study concluded the feasibility of direct deep learning-based approach to estimate Ki-Patlak parametric maps without requiring the input function and with a fewer number of passes. This would lead to shorter acquisition times for WB dynamic imaging with acceptable bias and comparable lesion detectability performance. [ABSTRACT FROM AUTHOR]