Acute myeloid leukemia with an unusual histologic pattern mimicking metastatic carcinoma in bone marrow: a diagnostic pitfall

Bone marrow metastasis in nonhematological malignancies: A study from tertiary care center

Introduction

Metastatic cancer presents a treatment challenge to clinicians, particularly for patients with bone marrow infiltration. For tumor staging, therapy selection, and prognosis risk stratification, the status of the bone marrow should be known for the presence or absence of metastasis. The study aimed to evaluate the hematological findings and comprehensive analysis of bone marrow in cases of nonhematological malignancies with bone marrow metastasis.

Materials and Methods

This retrospective study comprised a record retrieval of the departmental archives for the past 6 years. A total of 331 patients with nonhematological malignancies were found, of whom 31.42% (104/331) showed bone marrow metastasis. An integrated clinical approach with bone marrow examination findings and immunohistochemistry whenever necessary was used to achieve a definitive diagnosis of bone marrow metastasis.

Results

Among the study population, 31.42% (104/331) of patients had nonhematological malignancies that metastasized to the bone marrow. Most of the patients with bone marrow metastasis had anemia, which was found in 77.88% (81/104) of the cases. Leukoerythroblastic reaction was noted in 31.73% (33/104) of the cases, and thrombocytopenia was found in 25% (26/104) of the cases. The most common malignancy with bone marrow metastasis in adults was prostatic adenocarcinoma (28.1%) (9/32) and in pediatric cases, neuroblastoma (53.9%) (52/98).

Conclusions

It is essential to diagnose nonhematological malignancies that have metastasized to the bone marrow since this necessitates tumor staging, therapy selection, and prognosis risk stratification. To conclude, not a single hematological parameter is predictive of bone marrow metastasis; however, unexplained anemia, a leukoerythroblastic blood picture, and thrombocytopenia in peripheral blood should raise suspicion for bone marrow metastasis in cases of nonhematological malignancies.

Detection of Metastatic Tumor Cells in the Bone Marrow Aspirate Smears by Artificial Intelligence (AI)-Based Morphogo System

Introduction

Metastatic carcinomas of bone marrow (MCBM) are characterized as tumors of non-hematopoietic origin spreading to the bone marrow through blood or lymphatic circulation. The diagnosis is critical for tumor staging, treatment selection and prognostic risk stratification. However, the identification of metastatic carcinoma cells on bone marrow aspiration smears is technically challenging by conventional microscopic screening.

Objective

The aim of this study is to develop an automatic recognition system using deep learning algorithms applied to bone marrow cells image analysis. The system takes advantage of an artificial intelligence (AI)-based method in recognizing metastatic atypical cancer clusters and promoting rapid diagnosis.

Methods

We retrospectively reviewed metastatic non-hematopoietic malignancies in bone marrow aspirate smears collected from 60 cases of patients admitted to Zhongshan Hospital. High resolution digital bone marrow aspirate smear images were generated and automatically analyzed by Morphogo AI based system. Morphogo system was trained and validated using 20748 cell cluster images from randomly selected 50 MCBM patients. 5469 pre-classified cell cluster images from the remaining 10 MCBM patients were used to test the recognition performance between Morphogo and experienced pathologists.

Results

Morphogo exhibited a sensitivity of 56.6%, a specificity of 91.3%, and an accuracy of 82.2% in the recognition of metastatic cancer cells. Morphogo’s classification result was in general agreement with the conventional standard in the diagnosis of metastatic cancer clusters, with a Kappa value of 0.513. The test results between Morphogo and pathologists H1, H2 and H3 agreement demonstrated a reliability coefficient of 0.827. The area under the curve (AUC) for Morphogo to diagnose the cancer cell clusters was 0.865.

Conclusion

In patients with clinical history of cancer, the Morphogo system was validated as a useful screening tool in the identification of metastatic cancer cells in the bone marrow aspirate smears. It has potential clinical application in the diagnostic assessment of metastatic cancers for staging and in screening MCBM during morphology examination when the symptoms of the primary site are indolent.