Chapman, Christina

Abstract Cancer occurs in ∼1 per 1000 pregnancies; thousands of patients may require radiation procedures for diagnosis and treatment each year in the United States alone. This rare but high-risk scenario, coupled with fear of radiation, has created ambiguity in the ideal management of pregnant patients. Without a comprehensive guide for the use of radiation in imaging and treatment for pregnant cancer patients, it is difficult for providers to offer optimal patient-centered care without introducing disparities. The goal of this paper is to provide guidance on the use of radiation for screening, diagnosis, staging, and treatment of cancer, while highlighting gaps in existing knowledge and guidelines. The intention is that physicians, medical physicists, and patients could use this document as a resource for shared decision-making, ensuring safe and effective practice. A team of physicians and medical physicists with expertise in imaging, radiotherapy, and maternal fetal medicine was assembled, along with a patient advocate and lawyer. Existing guidelines and recent literature were reviewed. Authors also drew from their experience where published guidance was lacking. The resulting document discusses best practice to guide use of radiation for cancer, as well as patient-centered care management and legal considerations. Overall, it is possible to safely and effectively deliver radiation to pregnant patients in numerous circumstances. Use of radiation or other modalities should be discussed through shared decision-making with the physician and patient, contextualizing the maternal and fetal risk from treatments. Healthcare providers should support wide access to reproductive healthcare to allow equitable, evidence-based, patient-centered healthcare.

The Oncology Grand Rounds series is designed to place original reports published in the Journal into clinical context. A case presentation is followed by a description of diagnostic and management challenges, a review of the relevant literature, and a summary of the authors’ suggested management approaches. The goal of this series is to help readers better understand how to apply the results of key studies, including those published in Journal of Clinical Oncology , to patients seen in their own clinical practice.

Abstract Background Tuberculosis (TB) is an infectious disease that induces a complex response from the host immune system. Most carriers of the bacteria experience the asymptomatic, non-infectious phase, latent tuberculosis infection (LTBI), but have the potential to reactivate to active, contagious TB infection. Current diagnostics for TB are not sufficient for LTBI and are focused on using one biomarker, interferon-gamma (IFN-g). To build better diagnostics for the complex disease, multi-biomarker approaches to profile the immune system, coupled with machine learning algorithms, are necessary to diagnose LTBI and determine patients' risks of reactivation to active TB. We aim to use a multiplexed silicon photonic microring resonator sensing platform to profile thirteen biomarkers in patients with LTBI infection and use machine learning data analysis to identify biomarkers relevant for diagnosing LTBI and predicting reactivation risk. Methods We employed microring resonators coupled with a sandwich-style detection format to simultaneously detect thirteen cytokines and chemokines in plasma in under 40 minutes. Chip-integrated silicon photonic microrings are small in size and easily multiplexed with up to sixteen different capture agents. Biomolecular binding events are measured by monitoring the resonant wavelength within the sensing cavity, which shifts as binding events occur. Quantitation of each biomarker is possible with calibration curves that correlate standard protein concentrations to wavelength shifts. The multiplexed assay has been optimized for each individual biomarker, tested for cross-reactivity, and calibrated in the plasma matrix of interest. The LODs vary, depending on biomarker and matrix dilution, but range from 4.9 pg/mL to 691 pg/mL. The patient plasma samples are left over from the TB screening IFN-g release assay, QuantiFERON-TB Gold Plus (QFT), putting this method within the current clinical workflow. We use precision normalization approaches from the four QFT stimulations to account for immunologic differences among the subjects. The biomarker concentrations are evaluated using a random forest machine learning model to identify which biomarkers are important for identifying patients with LTBI and predicting their reactivation risk. Results We used this sensor method to profile 42 patients, 24 LTBI negative controls and 18 LTBI positive controls, with 13 being considered at high risk of reactivation. Using a precision normalization approach, a combination of nine normalized conditions using five of the thirteen biomarkers (CCL4, CCL8, IP-10, IL-2, IL-17) discriminated between LTBI positive and negative subjects with a ROC AUC of 0.90. Additionally, eight normalized conditions using four of the same biomarkers discriminated between high and low-risk subjects with an AUC of 0.83. Currently, we have expanded the sample cohort to include an additional 72 subjects, 25 LTBI positive and 47 LTBI negative. Preliminary analysis shows CCL8, IL-2, and IP-10 alone are able to distinguish between LTBI status (P values <0.01) using raw target concentrations prior to precision normalization. Conclusion Overall, we show the importance of multi-biomarker approaches to develop diagnostic and prognostic tools for a complex disease, such as TB. Further work to use the initial machine learning algorithm with the expanded cohort to validate biomarker importance is underway.