A Preliminary Investigation into Search and Matching for Tumor Discrimination in World Health Organization Breast Taxonomy Using Deep Networks.

Breast cancer is one of the most common cancers affecting women worldwide. It includes a group of malignant neoplasms with a variety of biological, clinical, and histopathologic characteristics. There are more than 35 different histologic forms of breast lesions that can be classified and diagnosed histologically according to cell morphology, growth, and architecture patterns. Recently, deep learning, in the field of artificial intelligence, has drawn a lot of attention for the computerized representation of medical images. Searchable digital atlases can provide pathologists with patch-matching tools, allowing them to search among evidently diagnosed and treated archival cases, a technology that may be regarded as computational second opinion. In this study, we indexed and analyzed the World Health Organization breast taxonomy (Classification of Tumors fifth ed.) spanning 35 tumor types. We visualized all tumor types using deep features extracted from a state-of-the-art deep-learning model, pretrained on millions of diagnostic histopathology images from the Cancer Genome Atlas repository. Furthermore, we tested the concept of a digital "atlas" as a reference for search and matching with rare test cases. The patch similarity search within the World Health Organization breast taxonomy data reached >88% accuracy when validating through "majority vote" and >91% accuracy when validating using top n tumor types. These results show for the first time that complex relationships among common and rare breast lesions can be investigated using an indexed digital archive.

A New Hierarchy of Research Evidence for Tumor Pathology: A Delphi Study to Define Levels of Evidence in Tumor Pathology.

The hierarchy of evidence is a fundamental concept in evidence-based medicine, but existing models can be challenging to apply in laboratory-based health care disciplines, such as pathology, where the types of evidence and contexts are significantly different from interventional medicine. This project aimed to define a comprehensive and complementary framework of new levels of evidence for evaluating research in tumor pathology-introducing a novel Hierarchy of Research Evidence for Tumor Pathology collaboratively designed by pathologists with help from epidemiologists, public health professionals, oncologists, and scientists, specifically tailored for use by pathologists-and to aid in the production of the World Health Organization Classification of Tumors (WCT) evidence gap maps. To achieve this, we adopted a modified Delphi approach, encompassing iterative online surveys, expert oversight, and external peer review, to establish the criteria for evidence in tumor pathology, determine the optimal structure for the new hierarchy, and ascertain the levels of confidence for each type of evidence. Over a span of 4 months and 3 survey rounds, we collected 1104 survey responses, culminating in a 3-day hybrid meeting in 2023, where a new hierarchy was unanimously agreed upon. The hierarchy is organized into 5 research theme groupings closely aligned with the subheadings of the WCT, and it consists of 5 levels of evidence-level P1 representing evidence types that merit the greatest level of confidence and level P5 reflecting the greatest risk of bias. For the first time, an international collaboration of pathology experts, supported by the International Agency for Research on Cancer, has successfully united to establish a standardized approach for evaluating evidence in tumor pathology. We intend to implement this novel Hierarchy of Research Evidence for Tumor Pathology to map the available evidence, thereby enriching and informing the WCT effectively.

Moving Forward on Tumor Pathology Research Reporting: A Guide for Pathologists From the World Health Organization Classification of Tumors Living Evidence Gap Map by Tumour Type Group.

Evidence-based medicine (EBM) can be an unfamiliar territory for those working in tumor pathology research, and there is a great deal of uncertainty about how to undertake an EBM approach to planning and reporting histopathology-based studies. In this article, reviewed and endorsed by the Word Health Organization International Agency for Research on Cancer's International Collaboration for Cancer Classification and Research, we aim to help pathologists and researchers understand the basics of planning an evidence-based tumor pathology research study, as well as our recommendations on how to report the findings from these. We introduce some basic EBM concepts, a framework for research questions, and thoughts on study design and emphasize the concept of reporting standards. There are many study-specific reporting guidelines available, and we provide an overview of these. However, existing reporting guidelines perhaps do not always fit tumor pathology research papers, and hence, here, we collate the key reporting data set together into one generic checklist that we think will simplify the task for pathologists. The article aims to complement our recent hierarchy of evidence for tumor pathology and glossary of evidence (study) types in tumor pathology. Together, these articles should help any researcher get to grips with the basics of EBM for planning and publishing research in tumor pathology, as well as encourage an improved standard of the reports available to us all in the literature.

Phyllodes tumour evidence gaps mapped from the 5th edition of the WHO classification of tumours of the breast.

AIMS: Breast phyllodes tumours (PTs) are a rare subset of fibroepithelial neoplasms categorised into benign, borderline, and malignant grades according to the World Health Organization (WHO) Classification of Tumours (WCTs). In this report, we developed an evidence gap map (EGM) based on the literature cited in the PT chapter of the 5th edition of the breast WCT in order to identify knowledge and research gaps in PT. METHODS: A framework was first established where the dimensions of the EGM were defined as categories of tumour descriptors, tumour types, and evidence levels. Citations were collected into a Microsoft Excel form and imported into EPPI-reviewer to produce the EGM. RESULTS: The EGM showed that the "Histopathology" and "Pathogenesis" sections contained the most citations, the majority being of low-level evidence. The highest number of citations considered of moderate-level evidence were found in the "Histopathology" section. There was no high-level evidence cited in this chapter. The "Localisation", "Aetiology", and "Staging" sections had the fewest citations. CONCLUSION: This EGM provides a visual representation of the cited literature in the PT chapter of the breast WCT, revealing the lack of high-level evidence citations. There is an uneven distribution of references, probably due to citation practices. Pockets of low-level evidence are highlighted, possibly related to referencing habits, lack of relevant research, or the belief that the information presented is standard accepted fact, without the need for specific citations. Future work needs to bridge evidence gaps and broaden citations beyond those in the latest WCT.

Diagnostic quality model (DQM): an integrated framework for the assessment of diagnostic quality when using AI/ML.

BACKGROUND: Laboratory medicine has reached the era where promises of artificial intelligence and machine learning (AI/ML) seem palpable. Currently, the primary responsibility for risk-benefit assessment in clinical practice resides with the medical director. Unfortunately, there is no tool or concept that enables diagnostic quality assessment for the various potential AI/ML applications. Specifically, we noted that an operational definition of laboratory diagnostic quality - for the specific purpose of assessing AI/ML improvements - is currently missing. METHODS: A session at the 3rd Strategic Conference of the European Federation of Laboratory Medicine in 2022 on "AI in the Laboratory of the Future" prompted an expert roundtable discussion. Here we present a conceptual diagnostic quality framework for the specific purpose of assessing AI/ML implementations. RESULTS: The presented framework is termed diagnostic quality model (DQM) and distinguishes AI/ML improvements at the test, procedure, laboratory, or healthcare ecosystem level. The operational definition illustrates the nested relationship among these levels. The model can help to define relevant objectives for implementation and how levels come together to form coherent diagnostics. The affected levels are referred to as scope and we provide a rubric to quantify AI/ML improvements while complying with existing, mandated regulatory standards. We present 4 relevant clinical scenarios including multi-modal diagnostics and compare the model to existing quality management systems. CONCLUSIONS: A diagnostic quality model is essential to navigate the complexities of clinical AI/ML implementations. The presented diagnostic quality framework can help to specify and communicate the key implications of AI/ML solutions in laboratory diagnostics.

Mucoepidermoid carcinoma (MEC) and adenosquamous carcinoma (ASC), the same or different entities?

Mucoepidermoid carcinoma (MEC) and adenosquamous carcinoma (ASC) have overlapping histopathological appearances and sites of occurrence, which may cause diagnostic difficulty impacting subsequent treatment. We conducted a systematic review of the scientific literature to determine whether molecular alterations were sufficiently different in MEC and ASC to aid in classifying the two entities. We searched Medline, Embase and Web of Science for studies reporting molecular determinations of ASC and/or MEC and screened retrieved records for eligibility. Two independent researchers reviewed included studies, assessed methodological quality and extracted data. Of 8623 identified records, 128 articles were included for analysis: 5 which compared the two tumors in the same investigation using the same methods and 123 which examined the tumors separately. All articles, except one were case series of moderate to poor methodological quality. The 5 publications examining both tumors showed that 52/88 (59%) MEC and 0% of 110 ASC had rearrangement of the MAML2 gene as detected by FISH and/or RT-PCR, but did not investigate other genes. In the entire series MEC had MAML2 gene rearrangement in 1337/2009 (66.6%) of tumors studied. The articles examining tumors separately found that MEC had mutations in EGFR (11/329 cases, 3.3%), KRAS (11/266, 4.1%) and ERBB2 (9/126, 7.1%) compared with ASC that had mutations in EGFR (660/1705, 38.7%), KRAS (143/625, 22.9%) and ERBB2 (6/196, 3.1%). The highest level of recurrent mutations was in pancreatic ASC where (108/126, 85.7%) reported mutations in KRAS. The EGFR mutations in ASC were similar in number and kind to those in lung adenocarcinoma. By standards of systematic review methodology and despite the large number of retrieved studies, we did not find adequate evidence for a distinctive molecular profile of either MEC or ASC that could definitively aid in its classification, especially in histologically difficult cases that are negative for MAML2 rearrangement. The case series included in this review indicate the relevance of MAML2 rearrangement to support the diagnosis of MEC, findings that should be confirmed by additional research with adequate study design.

Ki-67 assessment of pancreatic neuroendocrine neoplasms: Systematic review and meta-analysis of manual vs. digital pathology scoring.

Ki-67 assessment is a key step in the diagnosis of neuroendocrine neoplasms (NENs) from all anatomic locations. Several challenges exist related to quantifying the Ki-67 proliferation index due to lack of method standardization and inter-reader variability. The application of digital pathology coupled with machine learning has been shown to be highly accurate and reproducible for the evaluation of Ki-67 in NENs. We systematically reviewed all published studies on the subject of Ki-67 assessment in pancreatic NENs (PanNENs) employing digital image analysis (DIA). The most common advantages of DIA were improvement in the standardization and reliability of Ki-67 evaluation, as well as its speed and practicality, compared to the current gold standard approach of manual counts from captured images, which is cumbersome and time consuming. The main limitations were attributed to higher costs, lack of widespread availability (as of yet), operator qualification and training issues (if it is not done by pathologists), and most importantly, the drawback of image algorithms counting contaminating non-neoplastic cells and other signals like hemosiderin. However, solutions are rapidly developing for all of these challenging issues. A comparative meta-analysis for DIA versus manual counting shows very high concordance (global coefficient of concordance: 0.94, 95% CI: 0.83-0.98) between these two modalities. These findings support the widespread adoption of validated DIA methods for Ki-67 assessment in PanNENs, provided that measures are in place to ensure counting of only tumor cells either by software modifications or education of non-pathologist operators, as well as selection of standard regions of interest for analysis. NENs, being cellular and monotonous neoplasms, are naturally more amenable to Ki-67 assessment. However, lessons of this review may be applicable to other neoplasms where proliferation activity has become an integral part of theranostic evaluation including breast, brain, and hematolymphoid neoplasms.

WHO 2022 landscape of papillary and chromophobe renal cell carcinoma.

The 5th edition of the WHO Classification of Tumours of the Urinary and Male Genital Systems contains relevant revisions and introduces a group of molecularly defined renal tumour subtypes. Herein we present the World Health Organization (WHO) 2022 perspectives on papillary and chromophobe renal cell carcinoma with emphasis on their evolving classification, differential diagnosis, and emerging entities. The WHO 2022 classification eliminated the type 1/2 papillary renal cell carcinoma (pRCC) subcategorization, given the recognition of frequent mixed tumour phenotypes and the existence of entities with a different molecular background within the type 2 pRCC category. Additionally, emerging entities such as biphasic squamoid alveolar RCC, biphasic hyalinising psammomatous RCC, papillary renal neoplasm with reverse polarity, and Warthin-like pRCC are included as part of the pRCC spectrum, while additional morphological and molecular data are being gathered. In addition to oncocytomas and chromophobe renal cell carcinoma (chRCC), a category of 'other oncocytic tumours' with oncocytoma/chRCC-like features has been introduced, including emerging entities, most with TSC/mTOR pathway alterations (eosinophilic vacuolated tumour and so-called 'low-grade' oncocytic tumour), deserving additional research. Eosinophilic solid and cystic RCC was accepted as a new and independent tumour entity. Finally, a highly reproducible and clinically relevant universal grading system for chRCC is still missing and is another niche of ongoing investigation. This review discusses these developments and highlights emerging morphological and molecular data relevant for the classification of renal cell carcinoma.

WHO Classification of Tumours fifth edition: evolving issues in the classification, diagnosis, and prognostication of prostate cancer.

The fifth edition of the WHO Classification of Tumours of the Urinary and Male Genital Systems encompasses several updates to the classification and diagnosis of prostatic carcinoma as well as incorporating advancements in the assessment of its prognosis, including recent grading modifications. Some of the salient aspects include: (1) recognition that prostatic intraepithelial neoplasia (PIN)-like carcinoma is not synonymous with a pattern of ductal carcinoma, but better classified as a subtype of acinar adenocarcinoma; (2) a specific section on treatment-related neuroendocrine prostatic carcinoma in view of the tight correlation between androgen deprivation therapy and the development of prostatic carcinoma with neuroendocrine morphology, and the emerging data on lineage plasticity; (3) a terminology change of basal cell carcinoma to "adenoid cystic (basal cell) cell carcinoma" given the presence of an underlying MYB::NFIB gene fusion in many cases; (4) discussion of the current issues in the grading of acinar adenocarcinoma and the prognostic significance of cribriform growth patterns; and (5) more detailed coverage of intraductal carcinoma of prostate (IDC-P) reflecting our increased knowledge of this entity, while recommending the descriptive term atypical intraductal proliferation (AIP) for lesions falling short of IDC-P but containing more atypia than typically seen in high-grade prostatic intraepithelial neoplasia (HGPIN). Lesions previously regarded as cribriform patterns of HGPIN are now included in the AIP category. This review discusses these developments, summarising the existing literature, as well as the emerging morphological and molecular data that underpins the classification and prognostication of prostatic carcinoma.

Machine learning computational tools to assist the performance of systematic reviews: A mapping review.

BACKGROUND: Within evidence-based practice (EBP), systematic reviews (SR) are considered the highest level of evidence in that they summarize the best available research and describe the progress in a determined field. Due its methodology, SR require significant time and resources to be performed; they also require repetitive steps that may introduce biases and human errors. Machine learning (ML) algorithms therefore present a promising alternative and a potential game changer to speed up and automate the SR process. This review aims to map the current availability of computational tools that use ML techniques to assist in the performance of SR, and to support authors in the selection of the right software for the performance of evidence synthesis. METHODS: The mapping review was based on comprehensive searches in electronic databases and software repositories to obtain relevant literature and records, followed by screening for eligibility based on titles, abstracts, and full text by two reviewers. The data extraction consisted of listing and extracting the name and basic characteristics of the included tools, for example a tool's applicability to the various SR stages, pricing options, open-source availability, and type of software. These tools were classified and graphically represented to facilitate the description of our findings. RESULTS: A total of 9653 studies and 585 records were obtained from the structured searches performed on selected bibliometric databases and software repositories respectively. After screening, a total of 119 descriptions from publications and records allowed us to identify 63 tools that assist the SR process using ML techniques. CONCLUSIONS: This review provides a high-quality map of currently available ML software to assist the performance of SR. ML algorithms are arguably one of the best techniques at present for the automation of SR. The most promising tools were easily accessible and included a high number of user-friendly features permitting the automation of SR and other kinds of evidence synthesis reviews.

A common classification framework for histone sequence alterations in tumours: an expert consensus proposal.

The description of genetic alterations in tumours is of increasing importance. In human genetics, and in pathology reports, sequence alterations are given using the human genome variation society (HGVS) guidelines for the description of such variants. However, there is less adherence to these guidelines for sequence variations in histone genes. Due to early cleavage of the N-terminal methionine in most histones, the description of histone sequence alterations follows their own nomenclature and differs from the HGVS-compliant numbering by omitting this first amino acid. Next generation sequencing reports, however, follow the HGVS guidelines and as a result, an unambiguous description of sequence variants in histones cannot be provided. The coexistence of these two nomenclatures leads to confusions for pathologists, oncologists, and researchers. This review provides an overview of tumour entities with sequence alterations of the H3-3A gene (HGNC ID = HGNC:4764), highlights the problems associated with the coexistence of these two nomenclatures, and proposes a standard for the reporting of histone sequence variants that allows an unambiguous description of these variants according to HGVS principles. We hope that scientific journals will adopt the new notation, and that both geneticists and pathologists will include it in their reports. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Diagnostic Value of MAML2 Rearrangements in Mucoepidermoid Carcinoma.

Mucoepidermoid carcinoma (MEC) is often seen in salivary glands and can harbor MAML2 translocations (MAML2+). The translocation status has diagnostic utility as an objective confirmation of the MEC diagnosis, for example, when distinction from the more aggressive adenosquamous carcinoma (ASC) is not straightforward. To assess the diagnostic relevance of MAML2, we examined our 5-year experience in prospective testing of 8106 solid tumors using RNA-seq panel testing in combinations with a two-round Delphi-based scenario survey. The prevalence of MAML2+ across all tumors was 0.28% (n = 23/8106) and the majority of MAML2+ cases were found in head and neck tumors (78.3%), where the overall prevalence was 5.9% (n = 18/307). The sensitivity of MAML2 for MEC was 60% and most cases (80%) were submitted for diagnostic confirmation; in 24% of cases, the MAML2 results changed the working diagnosis. An independent survey of 15 experts showed relative importance indexes of 0.8 and 0.65 for "confirmatory MAML2 testing" in suspected MEC and ASC, respectively. Real-world evidence confirmed that the added value of MAML2 is a composite of an imperfect confirmation test for MEC and a highly specific exclusion tool for the diagnosis of ASC. Real-world evidence can help move a rare molecular-genetic biomarker from an emerging tool to the clinic.

Diagnosis of digestive system tumours.

The WHO Classification of Tumours provides the international standards for the classification and diagnosis of tumours. It enables direct comparisons to be made between different countries. In the new fifth edition, the series has gone digital with the launch of a website as well as a series of books, known widely as the WHO Blue Books. The first volume to be produced is on the classification of Digestive System tumours, replacing the successful 2010 version. It has been rewritten and updated accordingly. This article summarises the major diagnostic innovations that have occurred over the last decade and that have now been incorporated in the classification. As an example, it incorporates the recently proposed classification of neuroendocrine tumours, based on the recognition that neuroendocrine tumours and carcinomas differ substantially in the genetic abnormalities that drive their growth, findings relevant to treatment selection and outcome prediction. Several themes have emerged during the production process. One is the importance of the progression from hyperplasia to dysplasia to carcinoma in the evolution of the malignant process. Advances in imaging techniques and endoscopy have resulted in enhanced access to precancerous lesions in the gastrointestinal and biliary tract, necessitating both changes in classification schema and clinical practice. Diagnosis of tumours is no longer the sole purview of pathologists, and some patients now receive treatment before tissue is obtained, based on clinical, radiological and liquid biopsy results. This makes the classification relevant to many disciplines involved in the care of patients with tumours of the digestive system.

The International Collaboration for Cancer Classification and Research.

Gaps in the translation of research findings to clinical management have been recognized for decades. They exist for the diagnosis as well as the management of cancer. The international standards for cancer diagnosis are contained within the World Health Organization (WHO) Classification of Tumours, published by the International Agency for Research on Cancer (IARC) and known worldwide as the WHO Blue Books. In addition to their relevance to individual patients, these volumes provide a valuable contribution to cancer research and surveillance, fulfilling an important role in scientific evidence synthesis and international standard setting. However, the multidimensional nature of cancer classification, the way in which the WHO Classification of Tumours is constructed, and the scientific information overload in the field pose important challenges for the translation of research findings to tumour classification and hence cancer diagnosis. To help address these challenges, we have established the International Collaboration for Cancer Classification and Research (IC3 R) to provide a forum for the coordination of efforts in evidence generation, standard setting and best practice recommendations in the field of tumour classification. The first IC3 R meeting, held in Lyon, France, in February 2019, gathered representatives of major institutions involved in tumour classification and related fields to identify and discuss translational challenges in data comparability, standard setting, quality management, evidence evaluation and copyright, as well as to develop a collaborative plan for addressing these challenges.

Counting mitoses: SI(ze) matters!

Mitoses are often assessed by pathologists to assist the diagnosis of cancer, and to grade malignancy, informing prognosis. Historically, this has been done by expressing the number of mitoses per n high power fields (HPFs), ignoring the fact that microscope fields may differ substantially, even at the same high power (×400) magnification. Despite a requirement to define HPF size in scientific papers, many authors fail to address this issue adequately. The problem is compounded by the switch to digital pathology systems, where ×400 equivalent fields are rectangular and also vary in the area displayed. The potential for error is considerable, and at times this may affect patient care. This is easily solved by the use of standardized international (SI) units. We, therefore, recommend that features such as mitoses are always counted per mm2, with an indication of the area to be counted and the method used (usually "hotspot" or "average") to obtain the results.

Revising the WHO classification: female genital tract tumours.

The upcoming revision of the World Health Organisation (WHO) classification of tumours of the female genital tract is scheduled for release in the second quarter of 2020. It will feature significant changes compared to earlier editions. In this review, we outline the process of revising this important reference source for those diagnosing tumours or engaged in cancer research and describe the significant changes. The WHO classification of tumours is increasingly evidence-based, with a clear update cycle, improved quality of illustrations and content, led by an editorial board comprised mainly of pathologists, but increasingly incorporating input from other disciplines. The advent of the new website allows the use of whole-slide images and hyperlinks to evidence or external bodies that produce guidance on staging or reporting.

Gastrointestinal tissue-based molecular biomarkers: a practical categorisation based on the 2019 World Health Organization classification of epithelial digestive tumours.

Molecular biomarkers have come to constitute one of the cornerstones of oncological pathology. The method of classification not only directly affects the manner in which patients are diagnosed and treated, but also guides the development of drugs and of artificial intelligence tools. The aim of this article is to organise and update gastrointestinal molecular biomarkers in order to produce an easy-to-use guide for routine diagnostics. For this purpose, we have extracted and reorganised the molecular information on epithelial neoplasms included in the 2019 World Health Organization classification of tumours. Digestive system tumours, 5th edn.

Commentary: Cancer research quality and tumour classification.

Cancer researchers require accurate diagnoses for the samples, cell lines, patients or populations that they study. These diagnoses are underpinned by an internationally accepted taxonomy - the World Health Organization Classification of Tumours. This is still largely based on the histopathological examination of biopsy specimens, but increasingly also molecular methods and radiological examination of patients. Classifications evolve as new evidence arises, and for tumours that evidence is available in a quantity that is both remarkable and daunting. Evaluating this deluge of new information and incorporating it into the World Health Organization Classification of Tumours is now the responsibility of an editorial board, and up to 200 editors and authors work on each system to update it within the new 5th edition. Just as cancer researchers depend on the classification for diagnoses, so too the classification depends on the generation of high-quality, trustworthy data by cancer researchers. It is not just a case of quantity but quality too. Scientific fraud is thankfully rare, but high-profile cases are damaging and standards need to improve, not least to ensure that accurate information enters the classification.

A common classification framework for neuroendocrine neoplasms: an International Agency for Research on Cancer (IARC) and World Health Organization (WHO) expert consensus proposal.

The classification of neuroendocrine neoplasms (NENs) differs between organ systems and currently causes considerable confusion. A uniform classification framework for NENs at any anatomical location may reduce inconsistencies and contradictions among the various systems currently in use. The classification suggested here is intended to allow pathologists and clinicians to manage their patients with NENs consistently, while acknowledging organ-specific differences in classification criteria, tumor biology, and prognostic factors. The classification suggested is based on a consensus conference held at the International Agency for Research on Cancer (IARC) in November 2017 and subsequent discussion with additional experts. The key feature of the new classification is a distinction between differentiated neuroendocrine tumors (NETs), also designated carcinoid tumors in some systems, and poorly differentiated NECs, as they both share common expression of neuroendocrine markers. This dichotomous morphological subdivision into NETs and NECs is supported by genetic evidence at specific anatomic sites as well as clinical, epidemiologic, histologic, and prognostic differences. In many organ systems, NETs are graded as G1, G2, or G3 based on mitotic count and/or Ki-67 labeling index, and/or the presence of necrosis; NECs are considered high grade by definition. We believe this conceptual approach can form the basis for the next generation of NEN classifications and will allow more consistent taxonomy to understand how neoplasms from different organ systems inter-relate clinically and genetically.

Simultaneous detection of lung fusions using a multiplex RT-PCR next generation sequencing-based approach: a multi-institutional research study.

BACKGROUND: Gene fusion events resulting from chromosomal rearrangements play an important role in initiation of lung adenocarcinoma. The recent association of four oncogenic driver genes, ALK, ROS1, RET, and NTRK1, as lung tumor predictive biomarkers has increased the need for development of up-to-date technologies for detection of these biomarkers in limited amounts of material. METHODS: We describe here a multi-institutional study using the Ion AmpliSeq™ RNA Fusion Lung Cancer Research Panel to interrogate previously characterized lung tumor samples. RESULTS: Reproducibility between laboratories using diluted fusion-positive cell lines was 100%. A cohort of lung clinical research samples from different origins (tissue biopsies, tissue resections, lymph nodes and pleural fluid samples) were used to evaluate the panel. We observed 97% concordance for ALK (28/30 positive; 71/70 negative samples), 95% for ROS1 (3/4 positive; 19/18 negative samples), and 93% for RET (2/1 positive; 13/14 negative samples) between the AmpliSeq assay and other methodologies. CONCLUSION: This methodology enables simultaneous detection of multiple ALK, ROS1, RET, and NTRK1 gene fusion transcripts in a single panel, enhanced by an integrated analysis solution. The assay performs well on limited amounts of input RNA (10 ng) and offers an integrated single assay solution for detection of actionable fusions in lung adenocarcinoma, with potential savings in both cost and turn-around-time compared to the combination of all four assays by other methods.