Robotics-assisted automated staining of bone marrow biopsies: addressing the inconsistency in histological diagnosis of hairy cell leukemia variants

To the Editor, Hairy cell leukemia (HCL) variants are a rare and aggressive subtype of B-cell neoplasms corresponding to a significant change in their morphological and immunophenotypic characteristics, which mislead the diagnosis process in histology of the bone marrow biopsies[1]. Different interpretation of the histological results comes from subjective differences in manual staining, with an error rate of as much as 20–30% among pathologists for distinguishing HCL variants from classic HCL or other lymphoproliferative disorders. The HCL incidence rate is around 0.3–1.0 cases per 100 000 annually in the United States and Europe, with variants accounting for 10–15% of this total; therefore, it is essential to have a fast and precise diagnosis for downstream treatments like ibrutinib or venetoclax[2]. The manual staining protocols for bone marrow biopsy suffer from inter-observer variability, problems with antigen retrieval, and dye penetration in fibrotic marrow that increase the mistakes made in the identification of hairy cell projections or BRAF V600E mutations by immunohistochemistry[3]. Recent research indicates that automated staining systems, including robotics-assisted ones, have the ability to standardize chromogen application and have a reduction of artifacts by 40–50% when compared to manual methods in hematological specimens. One example is that robotic arms can do precise volumetric dispensing and provide real-time imaging feedback, thereby optimizing TRAP or annexin A1 staining that is crucial for HCL variants. These systems are connected to digital pathology scanners, thus allowing AI-driven hairy cell infiltration quantification, which has 85–90% agreement with expert consensus[3,4]. In a study published in Blood Advances in 2024, the application of robotics-assisted automated staining in the process of bone marrow biopsy of 48 cases of hairy cell leukemia was investigated, yielding an impressive 96% sensitivity for the co-expression of CD25/CD123 compared to only 79% for manual staining (P < 0.005), while AI-assisted digital quantification reduced the overestimation rate of fibrosis by 18%. This procedure also increased the accuracy of variant subtyping (classic vs. variant HCL) by 28%, which was linked to 28% longer progression-free survival due to ibrutinib dosing being precisely guided by BRAF V600E status. The training of algorithms on different cohorts was a significant challenge, but the operational efficiencies gained resulted in net cost savings within 18 months[5]. A deeper perception is being provided by Convolutional neural network applications in bone marrow cytology into using robotics to measure fibrosis and neoplastic burden, with prospective data indicating up to 15% higher diagnostic yield. Due to persisting limitations, the use of traditional manual methods over the WHO guidelines is still carried out as they meet the IHC standardization, in accordance with a local investigation conducted in academic hospitals[1,4]. New trials involving automated staining robots in myeloproliferative neoplasms have revealed that the robotic method provides an 18% increase in diagnostic yield that could easily be represented in hairy cell leukemia variants (HCL-v) via transfer learning on open-access datasets like TCGA and BeatAML. In resource-poor South Asian centers, for example, Lahore, manually processed bone marrow biopsies from patients with HCL-v have been shown to have 15–20% differences among observers due to inconsistencies in reagents, fatigue of pathologists, and increasing leukemia incidence (4–6/100 000). Automated processes maintain the WHO 5th edition standardization, including BRAF V600E and IGHV sequencing, while allowing liquid biopsy integration for ctDNA detection at 0.1% VAF, which is crucial for dry-tap marrows (40% of cases). This prevents diagnostic delays in high-burden settings, thus improving precision oncology workflows[6,7]. However, high costs and the lack of validation associated with low-tumor-burden HCL variants has made the integration of these technologies in routine diagnostics still slow. One approach is robotics-assisted staining, which virtually eliminates human error in sampling high-fibrosis areas, where classical HCL shows 70–90% marrow replacement, whereas variant forms often demonstrate less than 30%. Findings from pilot studies using automated systems demonstrate significant improvements in Ki-67 proliferation indexing and BRAF immunohistochemistry, resulting in a reduction in diagnostic turnaround time from days to hours[2,4]. The introduction of robotics-assisted automated systems has been stepping up as a major boon for the standardization of bone marrow biopsy analysis and lowering the error rates in the scenarios of BRAF V600E and annexin A1 biomarker identification in hairy cell leukemia variants, where inter-observer variability in manual staining has been causing major diagnostic delays[1,3]. These systems utilize exact dispensing and real-time feedback to reduce fibrotic sample artifacts and allow AI to quantify the tumor’s infiltration, resulting in an immunophenotypic sensitivity that no longer depends on traditional methods and is, in fact, shown to be superior as per recent studies[3–5]. It would be highly beneficial if pathologists and hematologists gave priority to multicenter trials that would allow the platforms to be validated, thus making the use of these advanced technologies more accessible in the developing regions that are in line with WHO precision oncology guidelines[1,4,6]. Academic institutions should work at developing partnerships that would lead to the creation of open-access datasets for transfer learning that would allow the convolutional neural networks to be adapted to the low-burden variants very quickly, thereby cutting down the delays in high-incidence areas[6,7]. The lawmakers should, through the application of cost-effective methods such as phased routine adoption, push the adoption of these technologies in a way that not only helps in gaining patient survival rates and hospital efficiency but also ensures that these gains are equitably distributed among the patients[2,5]. This letter to the editor adheres to the Transparency in the Reporting of Artificial Intelligence in Research (TITAN) guideline[8].