Natural Language Processing to Extract Meaningful Information from a Corpus of Written Knowledge in Breast Cancer: Transforming Books into Data.

Introduction: Books and papers are the most relevant source of theoretical knowledge for medical education. New technologies of artificial intelligence can be designed to assist in selected educational tasks, such as reading a corpus made up of multiple documents and extracting relevant information in a quantitative way. Methods: Thirty experts were selected transparently using an online public call on the website of the sponsor organization and on its social media. Six books edited or co-edited by members of this panel containing a general knowledge of breast cancer or specific surgical knowledge have been acquired. This collection was used by a team of computer scientists to train an artificial neural network based on a technique called Word2Vec. Results: The corpus of six books contained about 2.2 billion words for 300d vectors. A few tests were performed. We evaluated cosine similarity between different words. Discussion: This work represents an initial attempt to derive formal information from textual corpus. It can be used to perform an augmented reading of the corpus of knowledge available in books and papers as part of a discipline. This can generate new hypothesis and provide an actual estimate of their association within the expert opinions. Word embedding can also be a good tool when used in accruing narrative information from clinical notes, reports, etc., and produce prediction about outcomes. More work is expected in this promising field to generate "real-world evidence."

Should Acellular Dermal Matrices Be Used for Implant-based Breast Reconstruction after Mastectomy? Clinical Recommendation Based on the GRADE Approach.

Acellular dermal matrices (ADMs) entered the market in the early 2000s and their use has increased thereafter. Several retrospective cohort studies and single surgeon series reported benefits with the use of ADMs. However, robust evidence supporting these advantages is lacking. There is the need to define the role for ADMs in implant-based breast reconstruction (IBBR) after mastectomy. Methods: A panel of world-renowned breast specialists was convened to evaluate evidence, express personal viewpoints, and establish recommendation for the use of ADMs for subpectoral one-/two-stage IBBR (compared with no ADM use) for adult women undergoing mastectomy for breast cancer treatment or risk reduction using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach. Results: Based on the voting outcome, the following recommendation emerged as a consensus statement: the panel members suggest subpectoral one- or two-stage IBBR either with ADMs or without ADMs for adult women undergoing mastectomy for breast cancer treatment or risk reduction (with very low certainty of evidence). Conclusions: The systematic review has revealed a very low certainty of evidence for most of the important outcomes in ADM-assisted IBBR and the absence of standard tools for evaluating clinical outcomes. Forty-five percent of panel members expressed a conditional recommendation either in favor of or against the use of ADMs in subpectoral one- or two-stages IBBR for adult women undergoing mastectomy for breast cancer treatment or risk reduction. Future subgroup analyses could help identify relevant clinical and pathological factors to select patients for whom one technique could be preferable to another.

Evaluation of word embedding models to extract and predict surgical data in breast cancer.

BACKGROUND: Decisions in healthcare usually rely on the goodness and completeness of data that could be coupled with heuristics to improve the decision process itself. However, this is often an incomplete process. Structured interviews denominated Delphi surveys investigate experts' opinions and solve by consensus complex matters like those underlying surgical decision-making. Natural Language Processing (NLP) is a field of study that combines computer science, artificial intelligence, and linguistics. NLP can then be used as a valuable help in building a correct context in surgical data, contributing to the amelioration of surgical decision-making. RESULTS: We applied NLP coupled with machine learning approaches to predict the context (words) owning high accuracy from the words nearest to Delphi surveys, used as input. CONCLUSIONS: The proposed methodology has increased the usefulness of Delphi surveys favoring the extraction of keywords that can represent a specific clinical context. It permits the characterization of the clinical context suggesting words for the evaluation process of the data.

The Impact of the COVID-19 Pandemic on Surgical Management of Breast Cancer: Global Trends and Future Perspectives.

INTRODUCTION: The rapid spread of COVID-19 across the globe is forcing surgical oncologists to change their daily practice. We sought to evaluate how breast surgeons are adapting their surgical activity to limit viral spread and spare hospital resources. METHODS: A panel of 12 breast surgeons from the most affected regions of the world convened a virtual meeting on April 7, 2020, to discuss the changes in their local surgical practice during the COVID-19 pandemic. Similarly, a Web-based poll based was created to evaluate changes in surgical practice among breast surgeons from several countries. RESULTS: The virtual meeting showed that distinct countries and regions were experiencing different phases of the pandemic. Surgical priority was given to patients with aggressive disease not candidate for primary systemic therapy, those with progressive disease under neoadjuvant systemic therapy, and patients who have finished neoadjuvant therapy. One hundred breast surgeons filled out the poll. The trend showed reductions in operating room schedules, indications for surgery, and consultations, with an increasingly restrictive approach to elective surgery with worsening of the pandemic. CONCLUSION: The COVID-19 emergency should not compromise treatment of a potentially lethal disease such as breast cancer. Our results reveal that physicians are instinctively reluctant to abandon conventional standards of care when possible. However, as the situation deteriorates, alternative strategies of de-escalation are being adopted. IMPLICATIONS FOR PRACTICE: This study aimed to characterize how the COVID-19 pandemic is affecting breast cancer surgery and which strategies are being adopted to cope with the situation.