Interactions between Australian cancer physicians and the pharmaceutical industry: a qualitative study.

Objectives: To understand how and why Australian cancer physicians interact with the pharmaceutical industry.

Design: Qualitative study using semistructured interviews, performed by a medical oncologist. Thematic analysis using a combination of deductive and inductive codes.

Breast cancer characteristics and pathological prognostic determinants in indigenous Australians: Retrospective cohort study in the Northern Territory.

Background: There is a disparity in health outcomes between indigenous and nonindigenous Australians, with higher chronic disease burden and shorter life expectancy in this minority population. Although rates of breast cancer among indigenous women are lower than nonindigenous women, they face a higher breast cancer-associated mortality, which may not entirely be explained by socio-economic disadvantage.

Methods: This retrospective cohort study investigated previously described pathologic prognostic factors in indigenous Australians in the Northern Territory.

Communication of anticancer drug benefits and related uncertainties to patients and clinicians: document analysis of regulated information on prescription drugs in Europe.

Objective: To evaluate the frequency with which relevant and accurate information about the benefits and related uncertainties of anticancer drugs are communicated to patients and clinicians in regulated information sources in Europe.

Design: Document content analysis.

Setting: European Medicines Agency.

Australian Cancer Physicians and the Pharmaceutical Industry: A Survey of Attitudes and Interactions.

Purpose: Interactions between cancer physicians and the pharmaceutical industry may create conflicts of interest that can adversely affect patient care. We aimed to survey cancer physicians regarding their attitudes toward and interactions with industry.

Methods: We surveyed Australian cancer physicians between December 2020 and February 2021, questioning how often they interacted with industry and their attitudes toward this.

Interactions with the pharmaceutical industry and the practice, knowledge and beliefs of medical oncologists and clinical haematologists: a systematic review.

Background: No previous review has assessed the extent and effect of industry interactions on medical oncologists and haematologists specifically.

Methods: A systematic review investigated interactions with the pharmaceutical industry and how these might affect the clinical practice, knowledge and beliefs of cancer physicians. MEDLINE, Embase, PsycINFO and Web of Science Core Collection databases were searched from inception to February 2021.

Australia and New Zealand's responsibilities in improving oncology services in the Asia-Pacific: A call to action.

Aim: To review the expected increasing demand for cancer services among low and middle-income countries (LMICs) in the Asia-Pacific (APAC), and to describe ways in which Australia and New Zealand (ANZ) can provide support to improve cancer outcomes in our region.

Methods: We first review the current and projected incidence of cancer within the APAC between 2018 and 2040, and the estimated demand for chemotherapy, radiotherapy and surgery. We then explore potential ways in which ANZ can increase regional collaborations to improve cancer outcomes.

Industry payments to Australian medical oncologists and clinical haematologists: a cross-sectional analysis of publicly available disclosures.

Background: Payments to medical oncologists and clinical haematologists can negatively affect prescribing practice, but the extent of payments to these specialists is unknown in Australia.

Aims: To analyse the extent of payments from the pharmaceutical industry to Australian cancer physicians as reported during the first collated period of the Disclosure Australia website.

Methods: We performed a retrospective, cross-sectional analysis of payments made from November 2018 to April 2019, using a file downloaded from the Disclosure Australia website.

Thymic hyperplasia following double immune checkpoint inhibitor therapy in two patients with stage IV melanoma.

Hyperplasia of the thymus is commonly seen in myasthenia gravis and other autoimmune disorders. Thymic size also varies with age, corticosteroid use, infections, and inflammatory disease. Although thymic hyperplasia has been described following chemotherapy, there is no known association of true thymic hyperplasia with immune checkpoint inhibitor therapy.

Bilateral diffuse pulmonary infiltrates secondary to malignant peritoneal mesothelioma - A rare clinical presentation.

Diffuse pulmonary metastasis secondary to primary peritoneal malignant mesothelioma is rarely reported in the literature. In this report we describe a 59-year-old Caucasian women with no known previous asbestos exposure presenting with bilateral diffuse pulmonary opacities in association with primary malignant peritoneal mesothelioma. The diagnosis was confirmed by ultrasound guided abdominal and bronchoscopy, -bronchial lung biopsy.

Metastatic pancreatic ductal adenocarcinoma: diagnosis and treatment with a view to the future.

Metastatic pancreatic ductal adenocarcinoma (mPDAC) is a lethal disease with a poor 5-year survival. Systemic treatments can be used to control symptoms and prolong life. Cytotoxic chemotherapies are commonly administered, with combination treatments, such as fluorouracil, folinic acid, irinotecan and oxaliplatin (FOLFIRINOX) or nab-paclitaxel and gemcitabine showing the largest clinical benefits.

TEPAPA: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records.

Vast amounts of clinically relevant text-based variables lie undiscovered and unexploited in electronic medical records (EMR). To exploit this untapped resource, and thus facilitate the discovery of informative covariates from unstructured clinical narratives, we have built a novel computational pipeline termed Text-based Exploratory Pattern Analyser for Prognosticator and Associator discovery (TEPAPA). This pipeline combines semantic-free natural language processing (NLP), regular expression induction, and statistical association testing to identify conserved text patterns associated with outcome variables of clinical interest.

Computational prediction of multidisciplinary team decision-making for adjuvant breast cancer drug therapies: a machine learning approach.

Background: Multidisciplinary team (MDT) meetings are used to optimise expert decision-making about treatment options, but such expertise is not digitally transferable between centres. To help standardise medical decision-making, we developed a machine learning model designed to predict MDT decisions about adjuvant breast cancer treatments.

Methods: We analysed MDT decisions regarding adjuvant systemic therapy for 1065 breast cancer cases over eight years.