Portrait of Mustafa Basree

Mustafa Basree, DO

Radiation Oncology Resident

Department of Human Oncology


DO, University of Pikeville College of Osteopathic Medicine, (2021)

Intern, OhioHealth Riverside Methodist Hospital, (2022)

MS, The Ohio State University Graduate School, Anatomy (2017)

BS, The Ohio State University, Biological Sciences (2014)

Selected Honors and Awards

Excellence in Patient Care (2021)

Excellence in Research, KYCOM Award (2021)

Member, Gold Humanism Honor Society (2020)

ED to MED Outstanding Advocate of the Year Award, AACOM Government Relations (2019)

Dr. John A. Strosnider Leadership Award (2019)

Young Investigator Travel Award – Chinese Society for Clinical Oncology (2018)

Good Samaritan Service Award (2018)

American Society of Clinical Oncology Medical Student Rotation – Conquer Cancer Foundation Award (2018)

American Association for Cancer Research Scholar-in-Training Award (2018)

Pelotonia IRP Idea Grant (2016)

Boards, Advisory Committees and Professional Organizations

Member, UME-GME Transition to Residency Working Group, 2022-present

Member, AACOM’s Advisory Committee for Resilient Mindsets in Medicine, 2022-present

Membership Recruitment Chair, AOGME Residents and Fellows Council Executive Board , 2021-present

Associate Member, AOGME Residents and Fellows Council, 2021-present

American Society for Radiation Oncology, 2020-present

American College of Radiation Oncology, 2020-present

American Society of Clinical Oncology, 2019-present

Omega Beta Iota: National Osteopathic Political Honor Society, 2018-present

Sigma Sigma Phi: Osteopathic National Honors and Service Fraternity, 2018-present

Student Ambassador, ED to MED National Grassroots Advocacy Campaign, 2018-2021

Panelist, ED to MED Town Hall, AACOM Educating Leaders Conference, 2019

Delegate, Kentucky Osteopathic Medical Association, AOA House of Delegates, 2018-2019

Student Representative, University of Pikeville Board of Trustees, 2018-2019

President, Student Government Association, Kentucky College of Osteopathic Medicine, 2018-2019

Member, Social Media Steering Committee, University of Pikeville, 2018-2019

American Osteopathic Association, 2017-present

American Association for Cancer Research, 2017-2021

Sigma Xi: Scientific Research Honor Society, 2013-2019

  • Leveraging radiomics and machine learning to differentiate radiation necrosis from recurrence in patients with brain metastases Journal of neuro-oncology
    Basree MM, Li C, Um H, Bui AH, Liu M, Ahmed A, Tiwari P, McMillan AB, Baschnagel AM
    2024 Apr 30. doi: 10.1007/s11060-024-04669-4. Online ahead of print.
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      OBJECTIVE: Radiation necrosis (RN) can be difficult to radiographically discern from tumor progression after stereotactic radiosurgery (SRS). The objective of this study was to investigate the utility of radiomics and machine learning (ML) to differentiate RN from recurrence in patients with brain metastases treated with SRS.

      METHODS: Patients with brain metastases treated with SRS who developed either RN or tumor reccurence were retrospectively identified. Image preprocessing and radiomic feature extraction were performed using ANTsPy and PyRadiomics, yielding 105 features from MRI T1-weighted post-contrast (T1c), T2, and fluid-attenuated inversion recovery (FLAIR) images. Univariate analysis assessed significance of individual features. Multivariable analysis employed various classifiers on features identified as most discriminative through feature selection. ML models were evaluated through cross-validation, selecting the best model based on area under the receiver operating characteristic (ROC) curve (AUC). Specificity, sensitivity, and F1 score were computed.

      RESULTS: Sixty-six lesions from 55 patients were identified. On univariate analysis, 27 features from the T1c sequence were statistically significant, while no features were significant from the T2 or FLAIR sequences. For clinical variables, only immunotherapy use after SRS was significant. Multivariable analysis of features from the T1c sequence yielded an AUC of 76.2% (standard deviation [SD] ± 12.7%), with specificity and sensitivity of 75.5% (± 13.4%) and 62.3% (± 19.6%) in differentiating radionecrosis from recurrence.

      CONCLUSIONS: Radiomics with ML may assist the diagnostic ability of distinguishing RN from tumor recurrence after SRS. Further work is needed to validate this in a larger multi-institutional cohort and prospectively evaluate it's utility in patient care.

      PMID:38689115 | DOI:10.1007/s11060-024-04669-4

      View details for PubMedID 38689115
  • Treatment of Oligometastatic GI Cancers American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting
    Marcinak CT, Schwartz PB, Basree MM, Hurst N, Bassetti M, Kratz JD, Uboha NV
    2024 Jan;44:e430152. doi: 10.1200/EDBK_430152.
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      Oligometastatic state is believed to potentially represent a transitional stage between early, locoregional state disease and widely metastatic disease. Historically, locoregional approaches, particularly in advanced colorectal cancers, have demonstrated efficacy in select patients with limited burden of metastatic disease. Recent strides in systemic therapies, including biomarker-based treatments and immunotherapy, alongside innovations in surgical techniques and novel locoregional approaches such as stereotactic radiotherapy and ablation, have ushered in a new era of therapeutic possibilities across all oligometastatic GI cancers. Despite these advancements, there remains a significant gap in high-quality prospective evidence guiding patient selection and treatment decisions across various disease types. Ongoing clinical trials are anticipated to provide crucial insights into oligometastatic states, fostering the refinement of disease-specific oligometastatic state definitions and treatment algorithms. This article reviews existing data on the management of oligometastatic GI cancer, summarizes current state of knowledge for each disease state, and provides updates on ongoing studies in this space.

      PMID:38190577 | DOI:10.1200/EDBK_430152

      View details for PubMedID 38190577
  • Leveraging Quantitative Imaging and Machine Learning to Differentiate Radionecrosis from Disease Recurrence in Patients with Brain Metastases International journal of radiation oncology, biology, physics
    Basree MM, Li C, Bui AH, Liu M, Um H, Tiwari P, McMillan A, Baschnagel AM
    2023 Oct 1;117(2S):e85-e86. doi: 10.1016/j.ijrobp.2023.06.838.
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      PURPOSE/OBJECTIVE(S): Radiation necrosis can be difficult to non-invasively discern from tumor progression after stereotactic radiosurgery (SRS). In this work, we investigate the utility of radiomics (computerized features) and machine learning to capture per-voxel lesion heterogeneity on routine MRI scans, to differentiate radionecrosis from tumor recurrence in patients with brain metastases treated with SRS.

      MATERIALS/METHODS: A retrospective analysis was conducted of patients with brain metastases treated with SRS. Eighty-three lesions (n = 56 intact; n = 27 surgical cavity) from 69 patients were identified with median age 68.8 years (range 40.2 - 91.0), of whom 53.6% were male and 33.3% received prior whole-brain radiotherapy (WBRT). Lesion histology included lung (60.2%), renal cell (15.7%), melanoma (10.8%), breast (9.6%), and other (3.6%). Pathologic confirmation was available in 73.5% of lesions. Both intact and resection cavity lesions were included and individually segmented. Image preprocessing and radiomic feature extraction were done using ANTsPy and open-source software. A total of 210 features were extracted from post-contrast T1-weighted (T1w) and T2/FLAIR MRIs. Highly correlated features were removed. Univariate logistic regression was conducted on the remaining T1w and T2/FLAIR features as well as on clinical variables. Multivariate analysis was implemented with various classifiers (Random Forest, Ridge, Lasso, Support Vector Machine [SVM]) on the top-performing features found on univariate logistic regression. Models were assessed using cross-validation to select the best model by area under ROC curve (AUC). Specificity and sensitivity were calculated.

      RESULTS: On univariate analysis, the top 10 radiomics features consisted of 6 T1w features and 4 T2/FLAIR features (4 GLCM, 3 first order, 1 GLSZM, 1 GLRLM, and 1 shape feature). Age, gender, disease site, prior WBRT, prior fractionated SRS, planning tumor volume, brain-GTV V12 Gy, and immunotherapy before or after SRS were not predictive (AUC less than 62.0%) on univariate analysis compared to radiomic features. Multivariate analysis of top performing radiomic features on both intact and surgical cavities yielded an AUC of 72.0% (standard deviation [SD] ±8.8%). Multivariate analysis of top features on intact lesions alone improved the AUC to 80.5% (SD ±10.8%), with sensitivity of 77.8%, specificity of 72.4%, and positive likelihood ratio of 2.82 in differentiating radionecrosis from recurrence.

      CONCLUSION: Radiomics and machine learning tools may improve diagnostic ability of distinguishing radiation necrosis from tumor recurrence after SRS. Further work is needed to deploy this in a larger multi-institutional cohort and prospectively evaluate its efficacy as a decision-support tool to personalize care in patients with brain metastases.

      PMID:37786199 | DOI:10.1016/j.ijrobp.2023.06.838

      View details for PubMedID 37786199
  • Prediction of Rapid Early Progression and Survival Risk with Pre-Radiation MRI in WHO Grade 4 Glioma Patients Cancers
    Farzana W, Basree MM, Diawara N, Shboul ZA, Dubey S, Lockhart MM, Hamza M, Palmer JD, Iftekharuddin KM
    2023 Sep 19;15(18):4636. doi: 10.3390/cancers15184636.
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      Recent clinical research describes a subset of glioblastoma patients that exhibit REP prior to the start of radiation therapy. Current literature has thus far described this population using clinicopathologic features. To our knowledge, this study is the first to investigate the potential of conventional radiomics, sophisticated multi-resolution fractal texture features, and different molecular features (MGMT, IDH mutations) as a diagnostic and prognostic tool for prediction of REP from non-REP cases using computational and statistical modeling methods. The radiation-planning T1 post-contrast (T1C) MRI sequences of 70 patients are analyzed. An ensemble method with 5-fold cross-validation over 1000 iterations offers an AUC of 0.793 ± 0.082 for REP versus non-REP classification. In addition, copula-based modeling under dependent censoring (where a subset of the patients may not be followed up with until death) identifies significant features (p-value < 0.05) for survival probability and prognostic grouping of patient cases. The prediction of survival for the patients' cohort produces a precision of 0.881 ± 0.056. The prognostic index (PI) calculated using the fused features shows that 84.62% of REP cases fall under the bad prognostic group, suggesting the potential of fused features for predicting a higher percentage of REP cases. The experimental results further show that multi-resolution fractal texture features perform better than conventional radiomics features for prediction of REP and survival outcomes.

      PMID:37760604 | PMC:PMC10526762 | DOI:10.3390/cancers15184636

      View details for PubMedID 37760604
  • Progressive Global Ataxia With Sensory Changes as a Paraneoplastic Syndrome in a Patient With Chromophobe Renal Cell Carcinoma Cureus
    Basree MM, Rudy R, Romaniello C, Smith DE, Kander E
    2022 May 11;14(5):e24913. doi: 10.7759/cureus.24913. eCollection 2022 May.
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      Paraneoplastic syndromes (PNS) are rare and can be challenging to diagnose and treat. The uniqueness of PNS lies in the complexity of presentation, the importance of early diagnosis, and the role of multidisciplinary care in managing those patients to mitigate long-term neurologic complications. We describe a patient with metastatic renal cell carcinoma who presented with a complex constellation of neurological symptoms (progressive global ataxia and sensory changes) that did not resolve following nephrectomy. While complete resolution of symptoms was not achieved, he did have stabilization of his neurologic decline with the initiation of cancer-directed therapies.

      PMID:35698712 | PMC:PMC9187143 | DOI:10.7759/cureus.24913

      View details for PubMedID 35698712
  • Comprehensive Review of Molecular Mechanisms and Clinical Features of Invasive Lobular Cancer The oncologist
    Pramod N, Nigam A, Basree M, Mawalkar R, Mehra S, Shinde N, Tozbikian G, Williams N, Majumder S, Ramaswamy B
    2021 Jun;26(6):e943-e953. doi: 10.1002/onco.13734. Epub 2021 Mar 16.
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      Invasive lobular carcinoma (ILC) accounts for 10% to 15% of breast cancers in the United States, 80% of which are estrogen receptor (ER)-positive, with an unusual metastatic pattern of spread to sites such as the serosa, meninges, and ovaries, among others. Lobular cancer presents significant challenges in detection and clinical management given its multifocality and multicentricity at presentation. Despite the unique features of ILC, it is often lumped with hormone receptor-positive invasive ductal cancers (IDC); consequently, ILC screening, treatment, and follow-up strategies are largely based on data from IDC. Despite both being treated as ER-positive breast cancer, querying the Cancer Genome Atlas database shows distinctive molecular aberrations in ILC compared with IDC, such as E-cadherin loss (66% vs. 3%), FOXA1 mutations (7% vs. 2%), and GATA3 mutations (5% vs. 20%). Moreover, compared with patients with IDC, patients with ILC are less likely to undergo breast-conserving surgery, with lower rates of complete response following therapy as these tumors are less chemosensitive. Taken together, this suggests that ILC is biologically distinct, which may influence tumorigenesis and therapeutic strategies. Long-term survival and clinical outcomes in patients with ILC are worse than in stage- and grade-matched patients with IDC; therefore, nuanced criteria are needed to better define treatment goals and protocols tailored to ILC's unique biology. This comprehensive review highlights the histologic and clinicopathologic features that distinguish ILC from IDC, with an in-depth discussion of ILC's molecular alterations and biomarkers, clinical trials and treatment strategies, and future targets for therapy. IMPLICATIONS FOR PRACTICE: The majority of invasive lobular breast cancers (ILCs) are hormone receptor (HR)-positive and low grade. Clinically, ILC is treated similar to HR-positive invasive ductal cancer (IDC). However, ILC differs distinctly from IDC in its clinicopathologic characteristics and molecular alterations. ILC also differs in response to systemic therapy, with studies showing ILC as less sensitive to chemotherapy. Patients with ILC have worse clinical outcomes with late recurrences. Despite these differences, clinical trials treat HR-positive breast cancers as a single disease, and there is an unmet need for studies addressing the unique challenges faced by patients diagnosed with ILC.

      PMID:33641217 | PMC:PMC8176983 | DOI:10.1002/onco.13734

      View details for PubMedID 33641217
  • Abrupt involution induces inflammation, estrogenic signaling, and hyperplasia linking lack of breastfeeding with increased risk of breast cancer Breast cancer research : BCR
    Basree MM, Shinde N, Koivisto C, Cuitino M, Kladney R, Zhang J, Stephens J, Palettas M, Zhang A, Kim HK, Acero-Bedoya S, Trimboli A, Stover DG, Ludwig T, Ganju R, Weng D, Shields P, Freudenheim J, Leone GW, Sizemore GM, Majumder S, Ramaswamy B
    2019 Jul 17;21(1):80. doi: 10.1186/s13058-019-1163-7.
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      BACKGROUND: A large collaborative analysis of data from 47 epidemiological studies concluded that longer duration of breastfeeding reduces the risk of developing breast cancer. Despite the strong epidemiological evidence, the molecular mechanisms linking prolonged breastfeeding to decreased risk of breast cancer remain poorly understood.

      METHODS: We modeled two types of breastfeeding behaviors in wild type FVB/N mice: (1) normal or gradual involution of breast tissue following prolonged breastfeeding and (2) forced or abrupt involution following short-term breastfeeding. To accomplish this, pups were gradually weaned between 28 and 31 days (gradual involution) or abruptly at 7 days postpartum (abrupt involution). Mammary glands were examined for histological changes, proliferation, and inflammatory markers by immunohistochemistry. Fluorescence-activated cell sorting was used to quantify mammary epithelial subpopulations. Gene set enrichment analysis was used to analyze gene expression data from mouse mammary luminal progenitor cells. Similar analysis was done using gene expression data generated from human breast samples obtained from parous women enrolled on a tissue collection study, OSU-2011C0094, and were undergoing reduction mammoplasty without history of breast cancer.

      RESULTS: Mammary glands from mice that underwent abrupt involution exhibited denser stroma, altered collagen composition, higher inflammation and proliferation, increased estrogen receptor α and progesterone receptor expression compared to those that underwent gradual involution. Importantly, when aged to 4 months postpartum, mice that were in the abrupt involution cohort developed ductal hyperplasia and squamous metaplasia. Abrupt involution also resulted in a significant expansion of the luminal progenitor cell compartment associated with enrichment of Notch and estrogen signaling pathway genes. Breast tissues obtained from healthy women who breastfed for < 6 months vs ≥ 6 months showed significant enrichment of Notch signaling pathway genes, along with a trend for enrichment for luminal progenitor gene signature similar to what is observed in BRCA1 mutation carriers and basal-like breast tumors.

      CONCLUSIONS: We report here for the first time that forced or abrupt involution of the mammary glands following pregnancy and lack of breastfeeding results in expansion of luminal progenitor cells, higher inflammation, proliferation, and ductal hyperplasia, a known risk factor for developing breast cancer.

      PMID:31315645 | PMC:PMC6637535 | DOI:10.1186/s13058-019-1163-7

      View details for PubMedID 31315645
  • A hedgehog pathway-dependent gene signature is associated with poor clinical outcomes in Luminal A breast cancer Breast cancer research and treatment
    Rudolph M, Sizemore ST, Lu Y, Teng KY, Basree MM, Reinbolt R, Timmers CD, Leone G, Ostrowski MC, Majumder S, Ramaswamy B
    2018 Jun;169(3):457-467. doi: 10.1007/s10549-018-4718-x. Epub 2018 Feb 20.
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      PURPOSE: High expression of glioma-associated oncogene homolog-1 (GLI1) is associated with poor prognosis in estrogen receptor (ER) positive breast cancers. We sought to define a GLI1-dependent gene signature in ER-positive tumors that could further stratify patients at higher risk for disease recurrence and potentially lead to novel combination therapies.

      METHODS: We identified an inverse correlation between GLI1 expression and distant disease-free survival (DFS) using a dataset developed at MD Anderson Cancer Center (Hatzis dataset) containing clinical data from 508 breast cancer patients. Using a qPCR-based microarray platform, we identified genes differentially regulated by GLI1 in MCF7 cells and then determined if expression of these genes correlated with GLI1 expression in patient tumor samples. Statistical comparison between the groups was performed by ANOVA. Direct comparison of two groups was done by a two-tailed t test. Correlations between variables were done by Pearson's method.

      RESULTS: Expression of GLI1 and its target genes correlated significantly with worse distant DFS in breast cancer patients with Luminal A molecular subtype. Particularly, co-expression of GLI1 with EGFR and/or SNAI1, two of the identified GLI1 targets, was predictive of worse distant DFS in this subtype. Furthermore, patients with Luminal A tumors with a high GLI1 signature had a shorter distant DFS compared to the Luminal B subtype and the outcome for this group was comparable to patients with HER2-positive or basal-like tumors.

      CONCLUSION: We have identified a novel GLI1 gene signature that is associated with worse clinical outcomes among the patients with Luminal A subtype of breast cancer.

      PMID:29464534 | DOI:10.1007/s10549-018-4718-x

      View details for PubMedID 29464534
  • Erratum to: miR-29b defines the pro-/anti-proliferative effects of S100A7 in breast cancer Molecular cancer
    Zhao H, Wilkie T, Deol Y, Sneh A, Ganju A, Basree M, Nasser MW, Ganju RK
    2015 Nov 16;14(1):195. doi: 10.1186/s12943-015-0451-9.
  • miR-29b defines the pro-/anti-proliferative effects of S100A7 in breast cancer Molecular cancer
    Zhao H, Wilkie T, Deol Y, Sneh A, Ganju A, Basree M, Nasser MW, Ganju RK
    2015 Jan 27;14:11. doi: 10.1186/s12943-014-0275-z.
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      INTRODUCTION: S100A7 (Psoriasin) is an inflammatory protein known to be upregulated in breast cancer. However, the role of S100A7 in breast cancer has been elusive, since both pro- and anti-proliferative roles have been reported in different types of breast cancer cells and animal models. To date, the mechanism by which S100A7 differentially regulates breast cancer cell proliferation is still not clear.

      METHODS: We used Gene Functional Enrichment Analysis to search for the determining factor of S100A7 differential regulation. We confirmed the factor and elaborated its regulating mechanism using in vitro cell culture. We further verified the findings using xenografts of human breast cancer cells in nude mice.

      RESULTS: In the present study, we show that S100A7 significantly upregulates the expression of miR-29b in Estrogen Receptor (ER)-positive breast cancer cells (represented by MCF7), and significantly downregulates miR-29b in ER-negative cells (represented by MDA-MB-231) [Corrected]. The differential regulation of miR-29b by S100A7 in ER-positive and ER-negative breast cancer is supported by the gene expression analysis of TCGA invasive breast cancer dataset. miR-29b transcription is inhibited by NF-κB, and NF-κB activation is differentially regulated by S100A7 in ER-positive and ER-negative breast cancer cells. This further leads to differential regulation of PI3K p85α and CDC42 expression, p53 activation and p53-associated anti-proliferative pathways. Reversing the S100A7-caused changes of miR-29b expression by transfecting exogenous miR-29b or miR-29b-Decoy can inhibit the effects of S100A7 on in vitro cell proliferation and tumor growth in nude mice.

      CONCLUSIONS: The distinct modulations of the NF-κB - miR-29b - p53 pathway make S100A7 an oncogene in ER-negative and a cancer-suppressing gene in ER-positive breast cancer cells, with miR-29b being the determining regulatory factor.

      PMID:25622979 | PMC:PMC4314775 | DOI:10.1186/s12943-014-0275-z

      View details for PubMedID 25622979

Contact Information

Mustafa Basree, DO

600 Highland Avenue,
Madison, WI 53792