Primary splenic low-grade follicular lymphoma presenting with leukaemia and large cell transformation in the marrow

Low-Grade Primary Splenic CD10-Positive Small B-Cell Lymphoma/Follicular Lymphoma

Primary splenic lymphoma (PSL) is a rare malignancy representing about 1% of all lymphoproliferative disorders, when using a strict definition that allows only involvement of spleen and hilar lymph nodes. In contrast, secondary low-grade B-cell lymphomas in the spleen, such as follicular lymphomas (FL), lymphoplasmacytic lymphoma and chronic lymphocytic leukemia/ small lymphocytic lymphoma, particularly as part of advanced stage disease, are more common. Indolent B cell lymphomas expressing CD10 almost always represent FL, which in its primary splenic form is the focus of this review. Primary splenic follicular lymphoma (PSFL) is exceedingly infrequent. This type of lymphoproliferative disorder is understudied and, in most cases, clinically characterized by splenomegaly or cytopenias related to hypersplenism. The diagnosis requires correlation of histopathology of spleen, blood and/or bone marrow with the correct immunophenotype (determined by flow cytometry and/or immunohistochemistry) and if necessary, additional molecular profiling. Management of this incurable disease is evolving, and splenectomy remains the mainstream treatment for stage I PSFL.

Histopathologic and Machine Deep Learning Criteria to Predict Lymphoma Transformation in Bone Marrow Biopsies

CONTEXT.—

Large-cell transformation (LCT) of indolent B-cell lymphomas, such as follicular lymphoma (FL) and chronic lymphocytic leukemia (CLL), signals a worse prognosis, at which point aggressive chemotherapy is initiated. Although LCT is relatively straightforward to diagnose in lymph nodes, a marrow biopsy is often obtained first given its ease of procedure, low cost, and low morbidity. However, consensus criteria for LCT in bone marrow have not been established.

OBJECTIVE.—

To study the accuracy and reproducibility of a trained convolutional neural network in identifying LCT, in light of promising machine learning tools that may introduce greater objectivity to morphologic analysis.

DESIGN.—

We retrospectively identified patients who had a diagnosis of FL or CLL who had undergone bone marrow biopsy for the clinical question of LCT. We scored morphologic criteria and correlated results with clinical disease progression. In addition, whole slide scans were annotated into patches to train convolutional neural networks to discriminate between small and large tumor cells and to predict the patient's probability of transformation.

RESULTS.—

Using morphologic examination, the proportion of large lymphoma cells (≥10% in FL and ≥30% in CLL), chromatin pattern, distinct nucleoli, and proliferation index were significantly correlated with LCT in FL and CLL. Compared to pathologist-derived estimates, machine generated quantification demonstrated better reproducibility and stronger correlation with final outcome data.

CONCLUSIONS.—

These histologic findings may serve as indications of LCT in bone marrow biopsies. The pathologist-augmented with machine system appeared to be the most predictive, arguing for greater efforts to validate and implement these tools to further enhance physician practice.