Morphological analysis of the bone marrow is an essential step in the diagnosis of hematological disease. The conventional analysis of bone marrow smears is performed under a manual microscope, which is labor-intensive and subject to interobserver variability. The morphological differential diagnosis of abnormal lymphocytes from normal lymphocytes is still challenging. The digital pathology methods integrated with advances in machine learning enable new diagnostic features/algorithms from digital bone marrow cell images in order to optimize classification, thus providing a robust and faster screening diagnostic tool. We have developed a machine learning system, Morphogo, based on algorithms to discriminate abnormal lymphocytes from normal lymphocytes using digital imaging analysis. We retrospectively reviewed 347 cases of bone marrow digital images. Among them, 53 cases had a clinical history and the diagnosis of marrow involvement with lymphoma was confirmed either by morphology or flow cytometry. We split the 53 cases into two groups for training and testing with 43 and 10 cases, respectively. The selected 15,353 cell images were reviewed by pathologists, based on morphological visual appearance, from 43 patients whose diagnosis was confirmed by complementary tests. To expand the range and the precision of recognizing the lymphoid cells in the marrow by automated digital microscopy systems, we developed an algorithm that incorporated color and texture in addition to geometrical cytological features of the variable lymphocyte images which were applied as the training data set. The selected images from the 10 patients were analyzed by the trained artificial intelligence-based recognition system and compared with the final diagnosis rendered by pathologists. The positive predictive value for the identification of the categories of reactive/normal lymphocytes and abnormal lymphoid cells was 99.04%. It seems likely that further training and improvement of the algorithms will facilitate further subclassification of specific lineage subset pathology, e.g., diffuse large B-cell lymphoma from chronic lymphocytic leukemia/small lymphocytic lymphoma, follicular lymphoma, mantle cell lymphoma or even hairy cell leukemia in cases of abnormal malignant lymphocyte classes in the future. This research demonstrated the feasibility of digital pathology and emerging machine learning approaches to automatically diagnose lymphoma cells in the bone marrow based on cytological-histological analyses.

1.
Radakovich
N
,
Nagy
M
,
Nazha
A
.
Machine learning in haematological malignancies
.
Lancet Haematol
.
2020
Jul
;
7
(
7
):
e541
50
.
[PubMed]
2352-3026
2.
Fu
X
,
Fu
M
,
Li
Q
,
Peng
X
,
Lu
J
,
Fang
F
, et al
Morphogo: An Automatic Bone Marrow Cell Classification System on Digital Images Analyzed by Artificial Intelligence
.
Acta Cytol
.
2020
;
64
(
6
):
588
96
.
[PubMed]
1938-2650
3.
Jin
H
,
Fu
X
,
Cao
X
,
Sun
M
,
Wang
X
,
Zhong
Y
, et al
Developing and Preliminary Validating an Automatic Cell Classification System for Bone Marrow Smears: a Pilot Study
.
J Med Syst
.
2020
Sep
;
44
(
10
):
184
.
[PubMed]
1573-689X
4.
Alférez
S
,
Merino
A
,
Bigorra
L
,
Mujica
L
,
Ruiz
M
,
Rodellar
J
.
Automatic recognition of atypical lymphoid cells from peripheral blood by digital image analysis
.
Am J Clin Pathol
.
2015
Feb
;
143
(
2
):
168
76
.
[PubMed]
1943-7722
5.
Miyoshi
H
,
Sato
K
,
Kabeya
Y
,
Yonezawa
S
,
Nakano
H
,
Takeuchi
Y
, et al
Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma
.
Lab Invest
.
2020
Oct
;
100
(
10
):
1300
10
.
[PubMed]
1530-0307
6.
Alférez
S
,
Merino
A
,
Mujica
LE
,
Ruiz
M
,
Bigorra
L
,
Rodellar
J
.
Automatic classification of atypical lymphoid B cells using digital blood image processing
.
Int J Lab Hematol
.
2014
Aug
;
36
(
4
):
472
80
.
[PubMed]
1751-553X
Copyright / Drug Dosage / Disclaimer
Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.
You do not currently have access to this content.