Citations

The management of cancer has always relied heavily on the imaging modalities used to detect and monitor it. While many of these modalities have been around for decades, the technology surrounding them is always improving, and much has been discovered in recent years about the nature of tumors because of this. There have been several areas that have aided those discoveries. The use of artificial intelligence has already helped immensely in the quality of images taken but has not yet been widely implemented in clinical settings. Molecular imaging has proven to be useful in diagnosing different types of cancers based on the specificity of the probes/contrast agents used. Intravital imaging has already uncovered new information regarding the heterogeneity of the tumor vasculature. These three areas have provided a lot of useful information for the diagnosis and treatment of cancer, but further research and development in human trials is necessary to allow these techniques to fully utilize the information obtained thus far.

With the incidence of breast cancer steadily rising, it is important to explore novel technologies that can allow for earlier detection of disease as well more a personalized and effective treatment approach. The concept of “liquid biopsies” and the data they provide have been increasingly studied in the recent decades. More specifically, circulating tumor DNA (ctDNA) has emerged as a potential biomarker for various cancers, including breast cancer. While methods such as mammography and tissue biopsies are the current standards for the detection and surveillance of breast cancer, ctDNA analysis has shown some promise. This review discusses the versatility of ctDNA by exploring its multiple emerging uses for the management of breast cancer. Its efficacy is also compared to current biomarkers and technologies.

Abstract Background Limited data are available on the development of skin cancer and the associated risk factors for non‐White liver transplant (LT) recipients. The aim of this study is to determine the incidence of newly diagnosed skin cancer postoperatively and to identify the risk factors for the development of skin cancer in non‐White LT recipients. Methods We conducted an initial retrospective chart review of non‐White LT patients who received a transplant at our center between January 1, 2011, and December 31, 2013. Results Of the 96 patients in the study cohort, 32% were Black, 17% were Asian, 15% were White Hispanic, and 10% were Black Hispanic. One patient had a history of nonmelanoma skin cancer before transplant. No skin cancers were diagnosed during follow‐up (median, 1.3 years; range, 17 days to 8.6 years). Conclusion Our center’s experience is consistent with the literature and suggests that the incidence of newly diagnosed skin cancer in non‐White liver transplant recipients is low. Longer follow‐up may provide additional insights into the specific risk factors for the posttransplant development of skin cancer.

Abstract Background: DNIV accounts for 6%–10% of newly diagnosed breast cancer cases. Despite widespread mammography screening, its incidence is increasing in the United States and survival of this disease has only modestly improved since the late 1970s. As patient data accumulates in EHR, it’s promising to generate practice-based evidence through utilization of observational data sources. However, assembly of a DNIV cohort based on EHR data is challenging, as key pathologic and staging information are stored in unstructured clinical narratives and not available as structured data. In this study, we developed a rule-based algorithm to phenotype DNIV using natural language processing (NLP) techniques, and implemented the algorithm on our institutional EHR to extract potential DNIV cases. Methods and Results: We defined DNIV as those with either (1) M1 disease identified at time of initial presentation or M1 disease identified within 4 months after definitive surgery. We first developed a reference case list of DNIV verified by physician chart review of the EHR. We next refined the algorithm on a training dataset containing 51 positive and 38 negative reference cases. Next we tested the performance on the testing data containing 23 positive and 55 negative cases. The phenotyping algorithm identified key data elements using NLP, i.e., stage IV breast cancer, definitive surgery, stage 0-III, recurrent breast cancer and associated dates. To identify DNIV cases, phenotyping algorithm integrated temporal relations among the key data elements. The following steps were conducted in the following sequential order: (1) Identification of patients with breast cancer diagnosis using ICD-9 and ICD-10 codes. (2) Patients are positive cases if there are explicit mentions that delineate DNIV from recurrent metastatic breast cancer, such as “de novo stage IV” or “primary intact” detected by NLP at time of diagnosis. (3) Otherwise, patients with definitive surgery are selected if stage IV was within 5 years before definitive surgery or within 4 months after definitive surgery, along with patients with stage IV but without definitive surgery. (4) We further excluded patients with stage 0-III detected within 5 years after stage IV. (5) We further excluded patients with recurrent breast cancer detected before or at time of detection of stage IV. The remaining patients were left as positive cases. Precision of the algorithm was 70%, recall was 87% and F1, the weighted average of precision and recall, was 77%. We implemented our algorithm to interrogate 10 million clinical documents in a cohort of 56,548 patients with breast cancer diagnosis codes who presented to our institution between 2004 and 2018 and had research authorization. We identified 1918 potential DNIV cases. Conclusion: Our future focus is on algorithm refinement. An algorithm-generated cohort could serve as a data source for further study on outcomes related to DNIV and ideally as automated data abstractor and staging process. Citation Format: Liwei Wang, Karthik Giridhar, Kimberly Corbin, Brenda Ernst, Sadia Choudhery, Emmanuel Gabriel, Feichen Shen, Hongfang Liu, James Jakub. Identifying de novo stage IV breast cancer (DNIV) cases in Electronic Health Records (EHR) using natural language processing [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-050.

Background. Acral melanoma (AM) is a rare subtype of melanoma, which is one of the least common in Caucasian patients but is a common subtype of melanoma in Chinese patients. It is unclear if prognosis differs between Chinese and Caucasian patients diagnosed with AM. The aim of our study is to investigate patient characteristics and survival differences between Chinese and Caucasian AM patients. Methods. Two large institutional melanoma databases from Fudan University Shanghai Cancer Center (FUSCC) and Mayo Clinic enterprise were retrospectively reviewed from 2009 to 2015. Clinicopathologic and survival data were collected and analyzed between the two groups. The primary outcome was disease-specific survival (DSS) and was calculated using the Kaplan Meier (KM) method. Results. The Chinese group presented with more advanced disease compared with Caucasians: thicker Breslow depth (median 3.0 mm vs. 1.2 mm, p = 0.003 ), more ulcerated disease (66.1% vs. 29%; p < 0.001 ), and advanced stages (stage II/III 84.3% vs. 37.1%; p < 0.001 ). No significant difference was identified in terms of age at diagnosis, location, histologic subtypes, or node positive rate. The 5-year DSS rate was 68.4% and 73% ( p = 0.56 ) in Chinese and Caucasians AM patients, respectively. Male gender, Breslow thickness, ulceration, and positive sentinel lymph nodes were independent poor prognostic factors on multivariate analysis. Conclusions. There appears to be no difference in stage-stratified survival between Chinese and Caucasians, supporting the implementation of clinical trials for AM that could include both Chinese and Caucasian patients.

Background Recent studies have shown an increase in the incidence of gastric cancer (GC) among young adults in Asia and Latin America. However, it is unknown if a similar trend is happening in the United States (US). Methods A retrospective review of the National Cancer Data Base was conducted to identify patients diagnosed with gastric adenocarcinoma between the years of 2004 and 2013. Results A total of 93 734 patients were included. The 2 age groups below 40 did not see a change in GC incidence; however, age groups above 40 had increasing incidence. Patients aged 18-25 had the largest proportion of stage 4 disease and a poor survival (median 11.5 months), compared to older patients. Conclusion Despite the increasing trend of GC among individuals, the incidence of GC among young adults is not increasing. However, this subpopulation presents at more advanced stages (clinical stage 4) and thus has worse survival.