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Introduction: Our breast screening unit was identified as high outlier for B3 lesions with a low positive predictive value (PPV) compared to the England average. This prompted a detailed internal audit and review of B3 lesions and their outcomes to identify causes and address any variation in practice., Patients and Methods: The B3 rate was calculated in 4168 breast core biopsies from 2019, using the subsequent excision to determine the PPV. Atypical intraductal epithelial proliferation (AIDEP) cases were subject to microscopic review to reassess the presence of atypia against published criteria. The B3 rate was re-audited in 2021, and the results compared., Results: Screening cases had a high B3 rate of 12.4% (30% above the national average), and a PPV of 7.7% (9.7% with atypia). AIDEP was identified as a possible cause of this outlier status. On review and by consensus, AIDEP was confirmed in only 66% of cases reported as such, 17% were downgraded, and 16% did not reach consensus, the latter highlighting the difficulty and subjectivity in diagnosis of these lesions. Repeat audit of B3 rates after this extended review revealed a reduction from 12.4% to 9.11%, which is more in line with national standards., Conclusion: Benchmarking against national reporting standards is critical for service improvement. Through a supportive environment, team working, rigorous internal review and adherence to guidelines, interobserver variation and outlier status in breast pathology screening outliers can both be addressed. This study can serve as a model to other outlier units to identify and tackle underlying causes., (Copyright © 2022. Published by Elsevier Inc.)

The field of immuno-oncology has expanded rapidly over the past decade, but key questions remain. How does tumour-immune interaction regulate disease progression? How can we prospectively identify patients who will benefit from immunotherapy? Identifying measurable features of the tumour immune-microenvironment which have prognostic or predictive value will be key to making meaningful gains in these areas. Recent developments in deep learning enable big-data analysis of pathological samples. Digital approaches allow data to be acquired, integrated and analysed far beyond what is possible with conventional techniques, and to do so efficiently and at scale. This has the potential to reshape what can be achieved in terms of volume, precision and reliability of output, enabling data for large cohorts to be summarised and compared. This review examines applications of artificial intelligence (AI) to important questions in immuno-oncology (IO). We discuss general considerations that need to be taken into account before AI can be applied in any clinical setting. We describe AI methods that have been applied to the field of IO to date and present several examples of their use., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Purpose: Fresh-frozen (FF) tissue is the optimal source of DNA for whole-genome sequencing (WGS) of cancer patients. However, it is not always available, limiting the widespread application of WGS in clinical practice. We explored the viability of using formalin-fixed, paraffin-embedded (FFPE) tissues, available routinely for cancer patients, as a source of DNA for clinical WGS., Methods: We conducted a prospective study using DNAs from matched FF, FFPE, and peripheral blood germ-line specimens collected from 52 cancer patients (156 samples) following routine diagnostic protocols. We compared somatic variants detected in FFPE and matching FF samples., Results: We found the single-nucleotide variant agreement reached 71% across the genome and somatic copy-number alterations (CNAs) detection from FFPE samples was suboptimal (0.44 median correlation with FF) due to nonuniform coverage. CNA detection was improved significantly with lower reverse crosslinking temperature in FFPE DNA extraction (80 °C or 65 °C depending on the methods). Our final data showed somatic variant detection from FFPE for clinical decision making is possible. We detected 98% of clinically actionable variants (including 30/31 CNAs)., Conclusion: We present the first prospective WGS study of cancer patients using FFPE specimens collected in a routine clinical environment proving WGS can be applied in the clinic.