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Radiologist, Oncology Specialist (cancer), Radiation Oncology
30 years of experience
Accepting new patients


Education ?

Medical School Score
Rosalind Franklin University (1982)

Awards & Distinctions ?

American Board of Radiology
American Society for Therapeutic Radiology and Oncology

Affiliations ?

Dr. Smyczynski is affiliated with 3 hospitals.

Hospital Affiliations



  • U Mass Memorial Med Ctr - Memorial Campus
    Medical Oncology
    119 Belmont St, Worcester, MA 01605
    Top 25%
  • UMass Memorial Medical Center
  • UMass Memorial Health Care
  • Publications & Research

    Dr. Smyczynski has contributed to 3 publications.
    Title Superior Vena Cava Syndrome Secondary to an Angiotropic Large Cell Lymphoma.
    Date February 2001
    Journal Cancer

    BACKGROUND: Angiotropic large cell lymphoma (ALCL) is characterized by the intravascular proliferation of malignant lymphoid cells in small and medium-sized blood vessels. In the current study, the authors report an unusual case in which the initial presentation of the ALCL was that of superior vena cava (SVC) syndrome. METHODS: The case is presented, followed by a general review of the literature regarding ALCL. RESULTS: Surgical intervention was required for diagnosis in this case. Successful treatment with chemotherapy followed by involved field radiation ensued with a maintained disease remission at 48 months of follow-up. CONCLUSIONS: Although usually presenting in small blood vessels, ALCL can present initially with large blood vessel involvement and should be considered in the differential diagnosis of this condition, even in the absence of extravascular lymph node involvement. Aggressive treatment with antineoplastic therapy is warranted and may result in long term recurrence free survival.

    Title Impact of Respiratory Motion on the Detection of Small Pulmonary Nodules in Spect Imaging.
    Journal Ieee Nuclear Science Symposium Conference Record. Nuclear Science Symposium

    The objective of this investigation is to determine the impact of respiratory motion on the detection of small solitary pulmonary nodules (SPN) in single photon emission computed tomographic (SPECT) imaging. We have previously modeled the respiratory motion of SPN based on the change of location of anatomic structures within the lungs identified on breath-held CT images of volunteers acquired at two different stages of respiration. This information on respiratory motion within the lungs was combined with the end-expiration and time-averaged NCAT phantoms to allow the creation of source and attenuation maps for the normal background distribution of Tc-99m NeoTect. With the source and attenuation distribution thus defined, the SIMIND Monte Carlo program was used to produce SPECT projection data for the normal background and separately for each of 150 end-expiration and time-averaged simulated 1.0 cm tumors. Normal and tumor SPECT projection sets each containing one lesion were combined with a clinically realistic noise level and counts. These were reconstructed with RBI-EM using 1) no correction (NC), 2) attenuation correction (AC), 3) detector response correction (RC), and 4) attenuation correction, detector response correction, and scatter correction (AC_RC_SC). The post-reconstruction parameters of number of iterations and 3-D Gaussian filtering were optimized by human-observer studies. Comparison of lesion detection by human-observer LROC studies reveals that respiratory motion degrades tumor detection for all four reconstruction strategies, and that the magnitude of this effect is greatest for NC and RC, and least for AC_RC_SC. Additionally, the AC_RC_SC strategy results in the best detection of lesions.

    Title Estimation of Cardiac Respiratory-motion by Semi-automatic Segmentation and Registration of Non-contrast-enhanced 4d-ct Cardiac Datasets.
    Journal Ieee Transactions on Nuclear Science

    The goal of this work is to investigate, for a large set of patients, the motion of the heart with respiration during free-breathing supine medical imaging. For this purpose we analyzed the motion of the heart in 32 non-contrast enhanced respiratory-gated 4D-CT datasets acquired during quiet unconstrained breathing. The respiratory-gated CT images covered the cardiac region and were acquired at each of 10 stages of the respiratory cycle, with the first stage being end-inspiration. We devised a 3-D semi-automated segmentation algorithm that segments the heart in the 4D-CT datasets acquired without contrast enhancement for use in estimating respiratory motion of the heart. Our semi-automated segmentation results were compared against interactive hand segmentations of the coronal slices by a cardiologist and a radiologist. The pairwise difference in segmentation among the algorithm and the physicians was on the average 11% and 10% of the total average segmented volume across the patient, with a couple of patients as outliers above the 95% agreement limit. The mean difference among the two physicians was 8% with an outlier above the 95% agreement limit. The 3-D segmentation was an order of magnitude faster than the Physicians' manual segmentation and represents significant reduction of Physicians' time. The segmented first stages of respiration were used in 12 degree-of-freedom (DOF) affine registration to estimate the motion at each subsequent stage of respiration. The registration results from the 32 patients indicate that the translation in the superior-inferior direction was the largest component motion, with a maximum of 10.7 mm, mean of 6.4 mm, and standard deviation of 2.2 mm. Translation in the anterior-posterior direction was the next largest component of motion, with a maximum of 4.0 mm, mean of 1.7 mm, and standard deviation of 1.0 mm. Rotation about the right-left axis was on average the largest component of rotation observed, with a maximum of 4.6 degrees, mean of 1.6 degrees, and standard deviation of 2.1 degrees. The other rotation and shear parameters were all close to zero on average indicting the motion could be reasonably well approximated by rigid-body motion. However, the product of the three scale factors averaged about 0.97 indicating the possibility of a small decrease in heart volume with expiration. The motion results were similar whether we used the Physician's segmentation or the 3-D algorithm.

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