Abstract
Optimal treatment of breast cancer brain metastases (BCBM) is often hampered by limitations in diagnostic abilities. Developing innovative tools for BCBM diagnosis is vital for early detection and effective treatment. In this study we explored the advances in trial for the diagnosis of BCBM, with review of the literature. On May 8, 2019, we searched ClinicalTrials.gov for interventional and diagnostic clinical trials involving BCBM, without limiting for date or location. Information on trial characteristics, experimental interventions, results, and publications were collected and analyzed. In addition, a systematic review of the literature was conducted to explore published studies related to BCBM diagnosis. Only 9 diagnostic trials explored BCBM. Of these, 1 trial was withdrawn because of low accrual numbers. Three trials were completed; however, none had published results. Modalities in trial for BCBM diagnosis entailed magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), PET-CT, nanobodies, and circulating tumor cells (CTCs), along with a collection of novel tracers and imaging biomarkers. MRI continues to be the diagnostic modality of choice, whereas CT is best suited for acute settings. Advances in PET and PET-CT allow the collection of metabolic and functional information related to BCBM. CTC characterization can help reflect on the molecular foundations of BCBM, whereas cell-free DNA offers new genetic material for further exploration in trials. The integration of machine learning in BCBM diagnosis seems inevitable as we continue to aim for rapid and accurate detection and better patient outcomes.
| Original language | English (US) |
|---|---|
| Journal | Clinical breast cancer |
| DOIs | |
| State | Published - Jan 1 2019 |
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Keywords
- Artificial intelligence
- Circulating tumor cells
- Magnetic resonance imaging
- Nanobody
- Positron emission tomography
ASJC Scopus subject areas
- Oncology
- Cancer Research
Cite this
}
Diagnostic Clinical Trials in Breast Cancer Brain Metastases : Barriers and Innovations. / Fares, Jawad; Kanojia, Deepak; Rashidi, Aida; Ahmed, Atique Uddin; Balyasnikova, Irina V; Lesniak, Maciej S.
In: Clinical breast cancer, 01.01.2019.Research output: Contribution to journal › Review article
TY - JOUR
T1 - Diagnostic Clinical Trials in Breast Cancer Brain Metastases
T2 - Barriers and Innovations
AU - Fares, Jawad
AU - Kanojia, Deepak
AU - Rashidi, Aida
AU - Ahmed, Atique Uddin
AU - Balyasnikova, Irina V
AU - Lesniak, Maciej S
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Optimal treatment of breast cancer brain metastases (BCBM) is often hampered by limitations in diagnostic abilities. Developing innovative tools for BCBM diagnosis is vital for early detection and effective treatment. In this study we explored the advances in trial for the diagnosis of BCBM, with review of the literature. On May 8, 2019, we searched ClinicalTrials.gov for interventional and diagnostic clinical trials involving BCBM, without limiting for date or location. Information on trial characteristics, experimental interventions, results, and publications were collected and analyzed. In addition, a systematic review of the literature was conducted to explore published studies related to BCBM diagnosis. Only 9 diagnostic trials explored BCBM. Of these, 1 trial was withdrawn because of low accrual numbers. Three trials were completed; however, none had published results. Modalities in trial for BCBM diagnosis entailed magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), PET-CT, nanobodies, and circulating tumor cells (CTCs), along with a collection of novel tracers and imaging biomarkers. MRI continues to be the diagnostic modality of choice, whereas CT is best suited for acute settings. Advances in PET and PET-CT allow the collection of metabolic and functional information related to BCBM. CTC characterization can help reflect on the molecular foundations of BCBM, whereas cell-free DNA offers new genetic material for further exploration in trials. The integration of machine learning in BCBM diagnosis seems inevitable as we continue to aim for rapid and accurate detection and better patient outcomes.
AB - Optimal treatment of breast cancer brain metastases (BCBM) is often hampered by limitations in diagnostic abilities. Developing innovative tools for BCBM diagnosis is vital for early detection and effective treatment. In this study we explored the advances in trial for the diagnosis of BCBM, with review of the literature. On May 8, 2019, we searched ClinicalTrials.gov for interventional and diagnostic clinical trials involving BCBM, without limiting for date or location. Information on trial characteristics, experimental interventions, results, and publications were collected and analyzed. In addition, a systematic review of the literature was conducted to explore published studies related to BCBM diagnosis. Only 9 diagnostic trials explored BCBM. Of these, 1 trial was withdrawn because of low accrual numbers. Three trials were completed; however, none had published results. Modalities in trial for BCBM diagnosis entailed magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), PET-CT, nanobodies, and circulating tumor cells (CTCs), along with a collection of novel tracers and imaging biomarkers. MRI continues to be the diagnostic modality of choice, whereas CT is best suited for acute settings. Advances in PET and PET-CT allow the collection of metabolic and functional information related to BCBM. CTC characterization can help reflect on the molecular foundations of BCBM, whereas cell-free DNA offers new genetic material for further exploration in trials. The integration of machine learning in BCBM diagnosis seems inevitable as we continue to aim for rapid and accurate detection and better patient outcomes.
KW - Artificial intelligence
KW - Circulating tumor cells
KW - Magnetic resonance imaging
KW - Nanobody
KW - Positron emission tomography
UR - http://www.scopus.com/inward/record.url?scp=85068066053&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068066053&partnerID=8YFLogxK
U2 - 10.1016/j.clbc.2019.05.018
DO - 10.1016/j.clbc.2019.05.018
M3 - Review article
JO - Clinical Breast Cancer
JF - Clinical Breast Cancer
SN - 1526-8209
ER -