Concomitant Langerhans cell histiocytosis of cervical lymph nodes in adult patients with papillary thyroid carcinoma: A report of two cases and review of the literature

Objective: Langerhans cell histiocytosis (LCH) is an uncommon entity of unknown etiology. It contains a wide range of clinical presentations. The discovery of oncogenic BRAF V600E mutation in LCH has provided additional evidence that LCH is a neoplasm. Papillary thyroid carcinoma is the most common cancer of the thyroid characterized by a high incidence of BRAF V600E mutations. LCH with concomitant PTC is rare, with few cases reported in the literature. Cases summary: We identified two cases of LCH with concomitant papillary thyroid carcinoma in adult patients. The first was a 49-year-old female with a thyroid nodule diagnosed with papillary thyroid carcinoma. Later, the patient had a left neck mass; Ultrasound-guided lymph node FNA was diagnosed with Langerhans histiocytosis. Subsequently, a chest CT scan revealed signs of Langerhans cell histiocytosis in the lung. The second case refers to a 69-year-old male who presented with a left thyroid nodule diagnosed on FNA cytology as papillary thyroid carcinoma. The patient was found to have multiple bone lytic lesions. Biopsies revealed Langerhans cell histiocytosis. Later, the patient experienced LCH involvement of the bone marrow with associated secondary myelofibrosis. Conclusions: LCH is rare in adults; the association with papillary thyroid carcinoma is reported and should be considered in the presence of Langerhans cell groups along with PTC, whether in the thyroid gland or cervical lymph nodes. Once LCH has been diagnosed, pulmonary involvement should also be investigated. This will direct treatment plans for patients with pulmonary or systemic disease involvement

An update on the impact of SARS-CoV-2 pandemic public awareness on cancer patients' COVID-19 vaccine compliance: Outcomes and recommendations

Background:Aside from the pandemic's negative health effects, the world was confronted with public confusion since proper communication and favorable decisions became an ongoing challenge. As a result, the public's perceptions were influenced by what they knew, the many sources of COVID-19 information, and how they interpreted it. With cancer patients continuing to oppose COVID-19 vaccines, we sought to investigate the COVID-19 pandemic and vaccine sources of this information in adult cancer patients, which either helped or prevented them from taking the vaccine. We also assessed the relevance and impact of their oncologists' recommendations in encouraging them to take the vaccine.MethodsFrom June to October 2021, an online survey was conducted at King Hussein Cancer Center. A total of 441 adult cancer patients took part in the study. Patients who had granted their consent were requested to complete an online questionnaire, which was collected using the SurveyMonkey questionnaire online platform. Descriptive analysis was done for all variables. The association between categorical and continuous variables was assessed using the Pearson Chi-square and Fisher Exact.ResultsOur results showed that 75% of the patients registered for the COVID-19 vaccine, while 12% refused vaccination. The majority of participants acquired their information from news and television shows, whereas (138/441) got their information through World Health Organization websites. Because the SARS-CoV-2 vaccines were made in such a short period, 54.7 % assumed the vaccines were unsafe. Only 49% of the patients said their oncologists had informed them about the benefits of SARS-CoV-2 vaccines.ConclusionsWe found that SARS-CoV-2 vaccine hesitancy in cancer patients might be related to misinformation obtained from social media despite the availability of supportive scientific information on the vaccine's benefits from the physicians. To combat misleading and unreliable social media news, we recommend that physicians use telehealth technology to reach out to their patients in addition to their face-to-face consultation, which delivers comprehensive, clear, and high-quality digital services that guide and help patients to better understand the advantages of COVID-19 vaccines

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC

Spatial analyses of immune cell infiltration in cancer : current methods and future directions. A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.http://www.thejournalofpathology.com/hj2024ImmunologySDG-03:Good heatlh and well-bein

Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer

Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high-throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer

Non-Dexamethasone Corticosteroid Therapy’s Effect on COVID-19 Prognosis in Cancer Patients: A Retrospective Study

Background: Anti-inflammatory corticosteroids are used in cancer treatment and COVID-19 infections. Data on the impact of non-dexamethasone corticosteroids on COVID-19 infection severity in cancer patients are minimal. This study investigates if corticosteroid treatment affects the disease severity in adult cancer patients. Methods: A total of 116 COVID-19-infected cancer patients on hydrocortisone (H) or prednisone (P) were compared to 343 untreated patients. The study included patients who received corticosteroids before (B), after (A), or both before and after (B and A) COVID-19 infections. Ventilation support, hospitalization and mortality were investigated. Results: Our data showed that a significantly greater number of patients taking H or P required ventilation support and hospitalization and that mortality rates were higher than the control group. Patients who received H or P after COVID-19 infection had a significantly worse prognosis than the other sub-groups and the control group. Conclusion: Corticosteroids impacted cancer patients’ COVID-19 prognosis. Despite the limited sample size, H- and P-treated patients’ corticosteroids performed worse than the control, especially if treatments were received after COVID-19 infection. Hence, when a cancer patient already on H or P treatment is diagnosed with COVID-19, we recommend switching to a steroid treatment as suggested by international guidelines