Aims and objectives
Multiple Myeloma(MM) is a malignancy of plasma cells.
Expansion of single clone of plasma cell derived from B cells in bone marrow leads to multiple myeloma.
Patients present with anemia,
and bone destruction with pathologic fractures .Conventional radiography was used in the diagnosis but was hamperedbylow detection limit and sensitivity.
Among all imaging modalities for imaging marrow infiltration,
Magnetic Resonance (MR) imaging is most sensitive and specific.
T2- weighted (T2) and Diffusion Weighted (DW) imaging sequences are used for imagingmarrow...
Methods and materials
Patient Population: In this study,
MR images of 25 healthy individuals ( with average age of 51 years and standard deviation of 17 years) and 25 patient with myeloma (with average age of 56 years and standard deviation of 11 years) had been used.
Out of total 50 patient 18 were male and 32 were female.
Data Acquisition: All MR images were acquired using a 1.5 Tesla system (Achieva,
Philips Medical System).T1,
T2and diffusion sequences (b values 600 and 1200 s/mm2) were used in this...
Data were split in train set and test set in the ratio of 7:3.
Classifiers were trained on training set and test sets were used to check the performance of classifiers on unseen data sets.
Data sets were snuffled and split randomly.
In this fashion five set of data set were created and used for training and testing classifiers.
The mean of performance of all five datasetreported in the performance table.
Nomenclature of Data sets.
T1 dataset means feature extracted from T1 images.
The purpose of this study was to classify healthy individual versus individual ”diseased withmyeloma” using multi modal MR sequences.
Several classifiers were trained and tested on differentcombination of features which were extracted from axial pelvic images using different modality ofMR imaging.
Although we had limited annotated data,
we found classification accuracy of 92.3%when features of all images were used.
these classification algorithms can be used as screening tool to detect myelomatouslesions.
Medical Imaging and Reconstruction Lab.,
Department of Engineering Design,Indian Institute of Technology Madras,Chennai - 600036
Apollo CancerInstitute and Speciality Hospital,
Chennai - 600036,
RA Kyle and S Vincent Rajkumar.
Criteria for diagnosis,
risk stratification andresponse assessment of multiple myeloma.
Julie C Dutoit and Koenraad L Verstraete.
Mri in multiple myeloma: a pictorial review ofdiagnostic and post-treatment findings.
Insights into imaging,
HR D ̈urr,
and Maximilian Reiser.
Role of mri forthe diagnosis and prognosis of multiple myeloma.
European journal of radiology,
John D Shaughnessy Jr,