Laryngeal chondrosarcomas remain a rare disease of unknown etiology, with slow and insidious symptoms. The treatment is surgical, with favorable prognosis, and metastases rarely occur. The main concern regards their propensity to relapse.

Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples. In recent years, the pathology field has evolved to a digital era where tissue samples are digitised and evaluated on screen. As a result, digital pathology opened up many research opportunities, allowing the development of more advanced image processing techniques, as well as artificial intelligence (AI) methodologies. Nevertheless, despite colorectal cancer (CRC) being the second deadliest cancer type worldwide, with increasing incidence rates, the application of AI for CRC diagnosis, particularly on whole-slide images (WSI), is still a young field. In this review, we analyse some relevant works published on this particular task and highlight the limitations that hinder the application of these works in clinical practice. We also empirically investigate the feasibility of using weakly annotated datasets to support the development of computer-aided diagnosis systems for CRC from WSI. Our study underscores the need for large datasets in this field and the use of an appropriate learning methodology to gain the most benefit from partially annotated datasets. The CRC WSI dataset used in this study, containing 1,133 colorectal biopsy and polypectomy samples, is available upon reasonable request.

Background: The term phyllodes tumours, which account for less than 1% of breast neoplasms, describes a spectrum of heterogenous tumours with different clinical behaviours. Less than 30% present as metastatic disease. Complete surgical resection is the standard of care so that recurrence rates are reduced. The role of adjuvant chemotherapy or radiation therapy is controversial. Patients with metastatic disease have a median overall survival of around 30 months. Case description: The authors present the case of a 57-year-old woman with an exuberant left malignant phyllodes tumour with bilateral involvement, as well as lung and axillar metastasis. The patient underwent haemostatic radiation therapy and started palliative chemotherapy with doxorubicin, achieving partial response with significant improvement in quality of life. A posterior simple mastectomy revealed a small residual tumour. Discussion: Metastatic malignant phyllodes tumours are rare, so therapeutic strategies rely on small retrospective studies and guidelines for soft tissue sarcoma. Palliative chemotherapy protocols include anthracycline-based regimens, either as monotherapy with doxorubicin or doxorubicin together with ifosfamide. With few treatment options, management of these patients must rely on a continuum of care

Colorectal cancer (CRC) diagnosis is based on samples obtained from biopsies, assessed in pathology laboratories. Due to population growth and ageing, as well as better screening programs, the CRC incidence rate has been increasing, leading to a higher workload for pathologists. In this sense, the application of AI for automatic CRC diagnosis, particularly on whole-slide images (WSI), is of utmost relevance, in order to assist professionals in case triage and case review. In this work, we propose an interpretable semi-supervised approach to detect lesions in colorectal biopsies with high sensitivity, based on multiple-instance learning and feature aggregation methods. The model was developed on an extended version of the recent, publicly available CRC dataset (the CRC+ dataset with 4433 WSI), using 3424 slides for training and 1009 slides for evaluation. The proposed method attained 90.19% classification ACC, 98.8% sensitivity, 85.7% specificity, and a quadratic weighted kappa of 0.888 at slide-based evaluation. Its generalisation capabilities are also studied on two publicly available external datasets.

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Objective COVID-19 vaccines have shown efficacy and safety in healthy people. However, cancer patients under active immunosuppressive treatment were not included in the clinical trials conducted to test vaccines' efficacy and safety. This study aimed to evaluate the COVID-19 vaccine acceptance in cancer patients undergoing immunosuppressive therapy. Methods A total of 200 adult cancer patients received a questionnaire between March 8 and April 2, 2021, before the beginning of cancer patients' vaccination in Portugal. The questionnaire adapted from previously conducted studies included 11 close-ended items, evaluating variables such as patient sociodemographic and clinical characteristics, and the acceptance and underlying reasons to be or not to be vaccinated. The primary outcome was the intended acceptance of the COVID-19 vaccine in cancer patients. Multiple logistic regression was performed to identify factors associated with intended acceptance. Results Among the 200 delivered questionnaires, only 169 were included in this study. From those, 142 (84%) patients intended to be vaccinated against COVID-19. Only 27 participants (16%) had not yet decided or were reluctant to COVID-19 vaccination. High school degree (odds ratio (OR) 0.133, 95% confidence interval (C.I.) 0.031-0.579, p = 0.007], rural residence (OR 0.282, 95% C.I. 0.081-0.984, p = 0.047), and reluctance in believing in the vaccine efficacy (OR 0.058, 95% C.I. 0.016-0.204, p < 0.001] were identified predictors factor for COVID-19 vaccine hesitancy. Conclusion Most patients intended to be vaccinated against COVID-19, and specific factors such as education level, rural residence and the belief in vaccine efficacy were related to vaccine acceptance.

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Digital pathology (DP) is being deployed in many pathology laboratories, but most reported experiences refer to public health facilities. In this paper, we report our experience in DP transition at a high-volume private laboratory, addressing the main challenges in DP implementation in a private practice setting and how to overcome these issues. We started our implementation in 2020 and we are currently scanning 100% of our histology cases. Pre-existing sample tracking infrastructure facilitated this process. We are currently using two high-capacity scanners (Aperio GT450DX) to digitize all histology slides at 40×. Aperio eSlide Manager WebViewer viewing software is bidirectionally linked with the laboratory information system. Scanning error rate, during the test phase, was 2.1% (errors detected by the scanners) and 3.5% (manual quality control). Pre-scanning phase optimizations and vendor feedback and collaboration were crucial to improve WSI quality and are ongoing processes. Regarding pathologists’ validation, we followed the Royal College of Pathologists recommendations for DP implementation (adapted to our practice). Although private sector implementation of DP is not without its challenges, it will ultimately benefit from DP safety and quality-associated features. Furthermore, DP deployment lays the foundation for artificial intelligence tools integration, which will ultimately contribute to improving patient care.