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

Challenges and Clinical Relevance in Diagnosing Metastatic Cells From Non-Hematopoietic Malignancies in Bone Marrow Aspirates

INTRODUCTION

The causes of cytopenias are numerous, and the bone marrow aspirate helps to identify them. In rare cases, these cytopenias are due to bone marrow metastases from solid cancers. The techniques used in hematology laboratories are limited in characterizing these cells. Interaction with the cytopathology laboratory becomes critical for characterizing tumor cells and completing a comprehensive diagnosis from the bone marrow aspirate.

METHODS

This article describes a series of 38 bone marrow aspirates from 36 patients with bicytopenias who underwent bone marrow aspiration and for whom the hematologists sent the sample to the cytopathology laboratory to complete the diagnosis by immunocytochemistry and FISH if necessary.

RESULTS

The mean age of patients is 66 years, and the sex ratio is 2.8. Metastases were found in 11 cases of lung carcinoma, 4 cases of prostate carcinoma, 2 cases of breast carcinoma, 1 case of kidney carcinoma, 1 case of glioblastoma, 1 case of Ewing's sarcoma, and 1 case of melanoma. Among them, bone marrow aspiration was the only method to establish the initial diagnosis for seven patients. In six cases, immunocytochemistry confirmed the presence of carcinoma cells but could not identify their origin. In seven cases, tumor cells were insufficient to be characterized by immunocytochemistry.

CONCLUSION

Collaboration between laboratories is essential for the management of bone marrow aspirates containing non-hematopoietic metastases. Bone marrow aspiration may be sufficient to diagnose solid tumors, enabling faster initiation of treatment for patients already at an advanced stage of their disease.

Artificial intelligence in cytopathological applications for cancer: a review of accuracy and analytic validity

BACKGROUND

Cytopathological examination serves as a tool for diagnosing solid tumors and hematologic malignancies. Artificial intelligence (AI)-assisted methods have been widely discussed in the literature for increasing sensitivity, specificity and accuracy in the diagnosis of cytopathological clinical samples. Many of these tools are also used in clinical practice. There is a growing body of literature describing the role of AI in clinical settings, particularly in improving diagnostic accuracy and providing predictive and prognostic insights.

METHODS

A comprehensive search for this systematic review was conducted using databases Google, PUBMED (n = 450) and Google Scholar (n = 1067) with the keywords "Artificial Intelligence" AND "cytopathological" and "fine needle aspiration" AND "Deep Learning" AND "Machine Learning" AND "Hematologic Disorders" AND "Lung Cancer" AND "Pap Smear" and "cervical cancer screening" AND "Thyroid Cancer" AND "Breast Cancer" and "Sensitivity" and "Specificity". The search focused on literature reviews and systematic reviews published in English language between 2020 and 2024. PRISMA guidelines were adhered to with studies included and excluded as depicted in a flowchart. 417 results were screened with 34 studies were chosen for this review.

RESULTS

In the screening of patients with cervical cancer, bone marrow and peripheral blood smears and benign and malignant lesions in the lung, AI-assisted methods, particularly machine learning and deep learning (a subset of machine learning) methods, were applied to cytopathological data. These methods yielded greater diagnostic accuracy, specificity and sensitivity and decreased interobserver variability. Data sets were collected for both training and validation. Human machine combined performance was also found to be comparable to standalone performance in comparison with medical performance as well.

CONCLUSIONS

The use of AI in the analysis of cytopathological samples in research and clinical settings is increasing, and the involvement of pathologists in AI workflows is becoming increasingly important.

Optimization of diagnosis and treatment of hematological diseases via artificial intelligence

Background

Optimizing the diagnosis and treatment of hematological diseases is a challenging yet crucial research area. Effective treatment plans typically require the comprehensive integration of cell morphology, immunology, cytogenetics, and molecular biology. These plans also consider patient-specific factors such as disease stage, age, and genetic mutation status. With the advancement of artificial intelligence (AI), more "AI + medical" application models are emerging. In clinical practice, many AI-assisted systems have been successfully applied to the diagnosis and treatment of hematological diseases, enhancing precision and efficiency and offering valuable solutions for clinical practice.

Objective

This study summarizes the research progress of various AI-assisted systems applied in the clinical diagnosis and treatment of hematological diseases, with a focus on their application in morphology, immunology, cytogenetics, and molecular biology diagnosis, as well as prognosis prediction and treatment.

Methods

Using PubMed, Web of Science, and other network search engines, we conducted a literature search on studies from the past 5 years using the main keywords "artificial intelligence" and "hematological diseases." We classified the clinical applications of AI systems according to the diagnosis and treatment. We outline and summarize the current advancements in AI for optimizing the diagnosis and treatment of hematological diseases, as well as the difficulties and challenges in promoting the standardization of clinical diagnosis and treatment in this field.

Results

AI can significantly shorten turnaround times, reduce diagnostic costs, and accurately predict disease outcomes through applications in image-recognition technology, genomic data analysis, data mining, pattern recognition, and personalized medicine. However, several challenges remain, including the lack of AI product standards, standardized data, medical-industrial collaboration, and the complexity and non-interpretability of AI systems. In addition, regulatory gaps can lead to data privacy issues. Therefore, more research and improvements are needed to fully leverage the potential of AI to promote standardization of the clinical diagnosis and treatment of hematological diseases.

Conclusion

Our results serve as a reference point for the clinical diagnosis and treatment of hematological diseases and the development of AI-assisted clinical diagnosis and treatment systems. We offer suggestions for further development of AI in hematology and standardization of clinical diagnosis and treatment.

Detection of circulating plasma cells in peripheral blood using deep learning-based morphological analysis

BACKGROUND

The presence of circulating plasma cells (CPCs) is an important laboratory indicator for the diagnosis, staging, risk stratification, and progression monitoring of multiple myeloma (MM). Early detection of CPCs in the peripheral blood (PB) followed by timely interventions can significantly improve MM prognosis and delay its progression. Although the conventional cell morphology examination remains the predominant method for CPC detection because of accessibility, its sensitivity and reproducibility are limited by technician expertise and cell quantity constraints. This study aims to develop an artificial intelligence (AI)-based automated system for a more sensitive and efficient CPC morphology detection.

METHODS

A total of 137 bone marrow smears and 72 PB smears from patients with at Zhongshan Hospital, Fudan University, were retrospectively reviewed. Using an AI-powered digital pathology platform, Morphogo, 305,019 cell images were collected for training. Morphogo's efficacy in CPC detection was evaluated with additional 184 PB smears (94 from patients with MM and 90 from those with other hematological malignancies) and compared with manual microscopy.

RESULTS

Morphogo achieved 99.64% accuracy, 89.03% sensitivity, and 99.68% specificity in classifying CPCs. At a 0.60 threshold, Morphogo achieved a sensitivity of 96.15%, which was approximately twice that of manual microscopy, with a specificity of 78.03%. Patients with CPCs detected by AI scanning had a significantly shorter median progression-free survival compared with those without CPC detection (18 months vs. 34 months, p< .01).

CONCLUSIONS

Morphogo is a highly sensitive system for the automated detection of CPCs, with potential applications in initial screening, prognosis prediction, and posttreatment monitoring for MM patients.

PLAIN LANGUAGE SUMMARY

Diagnosing and monitoring multiple myeloma (MM), a type of blood cancer, requires identifying and quantifying specific cells called circulating plasma cells (CPCs) in the blood. The conventional method for detecting CPCs is manual microscopic examination, which is time-consuming and lacks sensitivity. This study introduces a highly sensitive CPC detection method using an artificial intelligence-based system, Morphogo. It demonstrated remarkable sensitivity and accuracy, surpassing conventional microscopy. This advanced approach suggests that early and accurate CPC detection is achievable by morphology examination, making efficient CPC screening more accessible for patients with MM. This innovative system has the potential to be used in the diagnosis and risk assessment of MM.

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.

Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine

Artificial intelligence (AI) has opened new medical avenues and revolutionized diagnostic and therapeutic practices, allowing healthcare providers to overcome significant challenges associated with cost, disease management, accessibility, and treatment optimization. Prominent AI technologies such as machine learning (ML) and deep learning (DL) have immensely influenced diagnostics, patient monitoring, novel pharmaceutical discoveries, drug development, and telemedicine. Significant innovations and improvements in disease identification and early intervention have been made using AI-generated algorithms for clinical decision support systems and disease prediction models. AI has remarkably impacted clinical drug trials by amplifying research into drug efficacy, adverse events, and candidate molecular design. AI's precision and analysis regarding patients' genetic, environmental, and lifestyle factors have led to individualized treatment strategies. During the COVID-19 pandemic, AI-assisted telemedicine set a precedent for remote healthcare delivery and patient follow-up. Moreover, AI-generated applications and wearable devices have allowed ambulatory monitoring of vital signs. However, apart from being immensely transformative, AI's contribution to healthcare is subject to ethical and regulatory concerns. AI-backed data protection and algorithm transparency should be strictly adherent to ethical principles. Vigorous governance frameworks should be in place before incorporating AI in mental health interventions through AI-operated chatbots, medical education enhancements, and virtual reality-based training. The role of AI in medical decision-making has certain limitations, necessitating the importance of hands-on experience. Therefore, reaching an optimal balance between AI's capabilities and ethical considerations to ensure impartial and neutral performance in healthcare applications is crucial. This narrative review focuses on AI's impact on healthcare and the importance of ethical and balanced incorporation to make use of its full potential.

High-accuracy morphological identification of bone marrow cells using deep learning-based Morphogo system

Accurate identification and classification of bone marrow (BM) nucleated cell morphology are crucial for the diagnosis of hematological diseases. However, the subjective and time-consuming nature of manual identification by pathologists hinders prompt diagnosis and patient treatment. To address this issue, we developed Morphogo, a convolutional neural network-based system for morphological examination. Morphogo was trained using a vast dataset of over 2.8 million BM nucleated cell images. Its performance was evaluated using 508 BM cases that were categorized into five groups based on the degree of morphological abnormalities, comprising a total of 385,207 BM nucleated cells. The results demonstrated Morphogo's ability to identify over 25 different types of BM nucleated cells, achieving a sensitivity of 80.95%, specificity of 99.48%, positive predictive value of 76.49%, negative predictive value of 99.44%, and an overall accuracy of 99.01%. In most groups, Morphogo cell analysis and Pathologists' proofreading showed high intragroup correlation coefficients for granulocytes, erythrocytes, lymphocytes, monocytes, and plasma cells. These findings further validate the practical applicability of the Morphogo system in clinical practice and emphasize its value in assisting pathologists in diagnosing blood disorders.

Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders

Artificial intelligence (AI) is a rapidly evolving field of computer science that involves the development of computational programs that can mimic human intelligence. In particular, machine learning and deep learning models have enabled the identification and grouping of patterns within data, leading to the development of AI systems that have been applied in various areas of hematology, including digital pathology, alpha thalassemia patient screening, cytogenetics, immunophenotyping, and sequencing. These AI-assisted methods have shown promise in improving diagnostic accuracy and efficiency, identifying novel biomarkers, and predicting treatment outcomes. However, limitations such as limited databases, lack of validation and standardization, systematic errors, and bias prevent AI from completely replacing manual diagnosis in hematology. In addition, the processing of large amounts of patient data and personal information by AI poses potential data privacy issues, necessitating the development of regulations to evaluate AI systems and address ethical concerns in clinical AI systems. Nonetheless, with continued research and development, AI has the potential to revolutionize the field of hematology and improve patient outcomes. To fully realize this potential, however, the challenges facing AI in hematology must be addressed and overcome.

Cytomorphological evaluation of non-haematopoietic malignancies metastasizing to the bone marrow

Bone marrow (BM) is one of the rare but important site of metastasis of solid tumors. The key steps of metastasis include invasion, intravasation, circulation, extravasation, and colonization. Tumor cells may express some adhesion molecules that promote the transmigration to the marrow space and link them to the marrow stroma with subsequent engraftment. It is important to detect the bone marrow metastasis for initial clinical staging, therapeutic selection, prognostic risk stratification, assessment of response to therapy and predicting relapse. Prognosis of non-hematopoietic malignancies with BM metastasis is dismal. Due to occulting and atypical clinical manifestations, bone marrow metastases can be easily missed or misdiagnosed, leading to higher mortality rates. The important factors on which the prognosis of patients with bone marrow metastases depends are primary tumor site, performance status, platelet count, and therapeutic regimens (systemic chemotherapy or palliative/supportive care). Further, in cases with BM metastasis with unknown primary sites, misdiagnosis can lead to delayed initiation of therapy and increased mortality. BM metastasis is seen in less than 10% of patients with metastatic cancer and is common in lung, breast or prostate carcinoma. Bone marrow metastasis can be presented as the initial presentation with hematological changes and may be misdiagnosed as a primary haematopoietic disorder. Leucoerythoblastic blood picture is the most common peripheral blood smear finding indicating BM metastasis, may be an indicator of associated BM fibrosis. Bone marrow aspiration and biopsy with immunohistochemistry (IHC) is an easy, cost effective and gold standard method of detection of BM metastasis. BM biopsy is superior to bone marrow aspirate for detection of metastasis. Morphology of metastatic cells is as per the primary site of tumor. Immunohistochemistry is a useful adjunct to morphology in reaching a definitive diagnosis even in case with carcinoma unknown primary (CUP) and also in diagnosing case of unsuspected malignancies. Though bone marrow is not among the most common site of involvement in CUP, which are liver, bone, lymph nodes and lung. But BM, if involved, the site of origin is determined using the immunohistochemistry panel applied to the metastatic deposits based on the morphology The aim of the review is to discuss the hematological findings of non-haematopoietic malignancies metastasizing to the bone marrow, emphasizing on histomorphology with IHC and its significance in establishing primary diagnosis in clinically unsuspected cases.

The diagnostic role of complete MICM-P in metastatic carcinoma of bone marrow (MCBM) presented with atypical symptoms: A 7-year retrospective study of 45 cases in a single center

Metastatic carcinoma of bone marrow (MCBM) tends to present with atypical symptoms and can be easily misdiagnosed or miss diagnosed. This study was conducted to investigate the clinical-pathological and hematological characteristics of MCBM patients in order to develop strategies for early detection, staging, treatment selection and prognosis predicting. We retrospectively analyzed 45 patients with MCBM diagnosed by bone marrow biopsy in our hospital during the past 7 years. The clinical symptoms, hemogram and myelogram features, Hematoxylin and eosin staining and immunohistochemistry staining of bone marrow biopsies, location of primary carcinoma and corresponding treatment of the 45 MCBM patients were analyzed in this study. In total, 35 (77.9%) of all patients presented pains including bone pain (73.3%) as the main manifestation, and 37 (82.2%) patients had anemia. Metastatic cancer cells were found in only 22 patients (48.9%) upon bone marrow smear examination, but in all 45 patients by bone marrow biopsy. The bone marrow of 18 (40.0%) patients was dry extraction. Distribution of metastatic carcinoma was diffuse in 20 (44.4%) patients and multi-focal in 25 (55.6%) patients, complicated with myelofibrosis in 34 (75.6%) patients. For bone marrow biopsy immunohistochemistry, 97.8% of the patients were CD45-negative, while 75.6% of the patients were Cytokeratin-positive. There were 30 patients (66.7%) identified with primary malignancies. The overall survival (OS) of 1 year for MCBM patients was 6.7%. There was a trend that patients with cancer of known primary obtained better prognosis according to the survival curve, but the finding was not statistically significant with Log-rank P = .160. Complete MICM-P plays a significant role in early diagnosis of MCBM. Bone marrow biopsy combined with immunohistochemistry is an underappreciated method for the diagnosis of MCBM, which should be taken as part of regular tests as well as bone marrow smear. Understanding the clinical-pathological and hematological characteristics of MCBM and conducting bone marrow biopsy in time are of great significance for early detection and treatment selection.