Artificial Intelligence-Powered Imaging Biomarker Based on Mammography for Breast Cancer Risk Prediction.

The purposes of this study were to develop an artificial intelligence (AI) model for future breast cancer risk prediction based on mammographic images, investigate the feasibility of the AI model, and compare the AI model, clinical statistical risk models, and Mirai, a state of-the art deep learning algorithm based on screening mammograms for 1-5-year breast cancer risk prediction. We trained and developed a deep learning model using a total of 36,995 serial mammographic examinations from 21,438 women (cancer-enriched mammograms, 17.5%). To determine the feasibility of the AI prediction model, mammograms and detailed clinical information were collected. C-indices and area under the receiver operating characteristic curves (AUCs) for 1-5-year outcomes were obtained. We compared the AUCs of our AI prediction model, Mirai, and clinical statistical risk models, including the Tyrer-Cuzick (TC) model and Gail model, using DeLong's test. A total of 16,894 mammograms were independently collected for external validation, of which 4002 were followed by a cancer diagnosis within 5 years. Our AI prediction model obtained a C-index of 0.76, with AUCs of 0.90, 0.84, 0.81, 0.78, and 0.81, to predict the 1-5-year risks. Our AI prediction model showed significantly higher AUCs than those of the TC model (AUC: 0.57; p < 0.001) and Gail model (AUC: 0.52; p < 0.001), and achieved similar performance to Mirai. The deep learning AI model using mammograms and AI-powered imaging biomarkers has substantial potential to advance accurate breast cancer risk prediction.

Screening mammography performance according to breast density: a comparison between radiologists versus standalone intelligence detection.

BACKGROUND: Artificial intelligence (AI) algorithms for the independent assessment of screening mammograms have not been well established in a large screening cohort of Asian women. We compared the performance of screening digital mammography considering breast density, between radiologists and AI standalone detection among Korean women. METHODS: We retrospectively included 89,855 Korean women who underwent their initial screening digital mammography from 2009 to 2020. Breast cancer within 12 months of the screening mammography was the reference standard, according to the National Cancer Registry. Lunit software was used to determine the probability of malignancy scores, with a cutoff of 10% for breast cancer detection. The AI's performance was compared with that of the final Breast Imaging Reporting and Data System category, as recorded by breast radiologists. Breast density was classified into four categories (A-D) based on the radiologist and AI-based assessments. The performance metrics (cancer detection rate [CDR], sensitivity, specificity, positive predictive value [PPV], recall rate, and area under the receiver operating characteristic curve [AUC]) were compared across breast density categories. RESULTS: Mean participant age was 43.5 ± 8.7 years; 143 breast cancer cases were identified within 12 months. The CDRs (1.1/1000 examination) and sensitivity values showed no significant differences between radiologist and AI-based results (69.9% [95% confidence interval [CI], 61.7-77.3] vs. 67.1% [95% CI, 58.8-74.8]). However, the AI algorithm showed better specificity (93.0% [95% CI, 92.9-93.2] vs. 77.6% [95% CI, 61.7-77.9]), PPV (1.5% [95% CI, 1.2-1.9] vs. 0.5% [95% CI, 0.4-0.6]), recall rate (7.1% [95% CI, 6.9-7.2] vs. 22.5% [95% CI, 22.2-22.7]), and AUC values (0.8 [95% CI, 0.76-0.84] vs. 0.74 [95% CI, 0.7-0.78]) (all P < 0.05). Radiologist and AI-based results showed the best performance in the non-dense category; the CDR and sensitivity were higher for radiologists in the heterogeneously dense category (P = 0.059). However, the specificity, PPV, and recall rate consistently favored AI-based results across all categories, including the extremely dense category. CONCLUSIONS: AI-based software showed slightly lower sensitivity, although the difference was not statistically significant. However, it outperformed radiologists in recall rate, specificity, PPV, and AUC, with disparities most prominent in extremely dense breast tissue.

Type 4 Dual Left Anterior Descending Artery: A Case Report of a Rare Congenital Coronary Anomaly.

Dual left anterior descending artery (LAD) is a rare congenital coronary artery anomaly with a prevalence of approximately 1% in the general population. To date, 10 types of dual LAD artery anomalies have been reported. Among these, type 4 is one of the rarest. Knowledge and recognition of the dual LAD artery are important for correct diagnosis and planning of coronary bypass surgery and percutaneous coronary intervention. We report a case of a 59-year-old male with type 4 dual LAD artery who presented with dyspepsia and sweating for several months and had approximately 50%-70% stenosis in a major diagonal branch off the short LAD artery.

Use of a commercial artificial intelligence-based mammography analysis software for improving breast ultrasound interpretations.

OBJECTIVES: To evaluate the use of a commercial artificial intelligence (AI)-based mammography analysis software for improving the interpretations of breast ultrasound (US)-detected lesions. METHODS: A retrospective analysis was performed on 1109 breasts that underwent both mammography and US-guided breast biopsy. The AI software processed mammograms and provided an AI score ranging from 0 to 100 for each breast, indicating the likelihood of malignancy. The performance of the AI score in differentiating mammograms with benign outcomes from those revealing cancers following US-guided breast biopsy was evaluated. In addition, prediction models for benign outcomes were constructed based on clinical and imaging characteristics with and without AI scores, using logistic regression analysis. RESULTS: The AI software had an area under the receiver operating characteristics curve (AUROC) of 0.79 (95% CI, 0.79-0.82) in differentiating between benign and cancer cases. The prediction models that did not include AI scores (non-AI model), only used AI scores (AI-only model), and included AI scores (integrated model) had AUROCs of 0.79 (95% CI, 0.75-0.83), 0.78 (95% CI, 0.74-0.82), and 0.85 (95% CI, 0.81-0.88) in the development cohort, and 0.75 (95% CI, 0.68-0.81), 0.82 (95% CI, 0.76-0.88), and 0.84 (95% CI, 0.79-0.90) in the validation cohort, respectively. The integrated model outperformed the non-AI model in the development and validation cohorts (p < 0.001 for both). CONCLUSION: The commercial AI-based mammography analysis software could be a valuable adjunct to clinical decision-making for managing US-detected breast lesions. CLINICAL RELEVANCE STATEMENT: The commercial AI-based mammography analysis software could potentially reduce unnecessary biopsies and improve patient outcomes. KEY POINTS: • Breast US has high rates of false-positive interpretations. • A commercial AI-based mammography analysis software could distinguish mammograms having benign outcomes from those revealing cancers after US-guided breast biopsy. • A commercial AI-based mammography analysis software may improve interpretations for breast US-detected lesions.

Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine.

Breast cancer is a significant cause of cancer-related mortality in women worldwide. Early and precise diagnosis is crucial, and clinical outcomes can be markedly enhanced. The rise of artificial intelligence (AI) has ushered in a new era, notably in image analysis, paving the way for major advancements in breast cancer diagnosis and individualized treatment regimens. In the diagnostic workflow for patients with breast cancer, the role of AI encompasses screening, diagnosis, staging, biomarker evaluation, prognostication, and therapeutic response prediction. Although its potential is immense, its complete integration into clinical practice is challenging. Particularly, these challenges include the imperatives for extensive clinical validation, model generalizability, navigating the "black-box" conundrum, and pragmatic considerations of embedding AI into everyday clinical environments. In this review, we comprehensively explored the diverse applications of AI in breast cancer care, underlining its transformative promise and existing impediments. In radiology, we specifically address AI in mammography, tomosynthesis, risk prediction models, and supplementary imaging methods, including magnetic resonance imaging and ultrasound. In pathology, our focus is on AI applications for pathologic diagnosis, evaluation of biomarkers, and predictions related to genetic alterations, treatment response, and prognosis in the context of breast cancer diagnosis and treatment. Our discussion underscores the transformative potential of AI in breast cancer management and emphasizes the importance of focused research to realize the full spectrum of benefits of AI in patient care.

Clinical outcomes after incomplete cycles of R-CHOP for diffuse large B-cell lymphoma: 10 years' real-world experience in a single institute.

Although the current standard of care for diffuse large B-cell lymphoma (DLBCL) is six cycles of rituximab/cyclophosphamide/doxorubicin/vincristine/prednisolone combination chemotherapy (R-CHOP), a larger than expected number of patients cannot complete planned six cycles for various reasons in the real world. We aimed to evaluate the prognosis of patients with DLBCL after incomplete treatment by analyzing the chemotherapy response and survival according to the cause of discontinuation and the number of cycles. We analyzed a retrospective cohort of patients diagnosed with DLBCL who underwent incomplete cycles of R-CHOP at Seoul National University Hospital and Boramae Medical Center from January 2010 to April 2019. A total of 1183 patients were diagnosed with DLBCL, of which 260 (22%) did not complete six cycles of R-CHOP. The most common cause of discontinuation of chemotherapy was life-threatening infection, and the most common pathogen was Pneumocystis jirovecii. Overall survival (OS) and progression-free survival (PFS) were significantly better in patients who achieved complete response (CR) or partial response (PR) at the first response evaluation. Patients underwent three or more cycles of chemotherapy had a longer OS than those who did not. In patients with limited-stage disease, consolidative radiotherapy showed a significant improvement in OS and PFS. Advanced stage, high comorbidity score, and poor primary response to chemotherapy were poor prognostic factors in patients with unplanned treatment shortening. This study provides real-world outcomes for patients who could not complete the planned six cycles of R-CHOP.

Efficacy of Prophylactic Cranial Irradiation According to the Risk of Extracranial Recurrence in Limited-Stage Small Cell Lung Cancer.

PURPOSE: We aimed to evaluate the effectiveness of prophylactic cranial irradiation (PCI) for "early brain metastasis", which occurs before extracranial recurrence (ECR), and "late brain metastasis", which occurs after ECR, in limited-stage small cell lung cancer (LS-SCLC). Materials and Methods: We retrospectively analyzed 271 LS-SCLC patients who underwent definitive chemoradiation. All patients were initially staged with brain magnetic resonance imaging and positron emission tomography. Intracranial recurrence (ICR), ECR, progression-free rate (PFR), and overall survival (OS) were analyzed as clinical endpoints. The competing risk of the first recurrence with ICR (ICRfirst) was evaluated. Significantly associated variables in multivariate analysis of ECR were considered as ECR risk factors. Patients were stratified according to the number of ECR risk factors. RESULTS: The application of PCI was associated with higher PFR (p=0.008) and OS (p=0.045). However, PCI was not associated with any of the clinical endpoints in multivariate analysis. The competing risk of ICRfirst was significantly decreased with the application of PCI (hazard ratio, 0.476; 95% confidence interval, 0.243 to 0.931; p=0.030). Stage III disease, sequential, and stable disease after thoracic radiation were selected as ECR risk factors. For patients without these risk factors, the application of PCI was significantly associated with increased OS (p=0.048) and a decreased risk of ICRfirst (p=0.026). CONCLUSION: PCI may play a role in preventing early brain metastasis rather than late brain metastasis after ECR, suggesting that only patients with a low risk of ECR may currently benefit from PCI.

Prognostic roles of leptin-signaling proteins, PD-L1, and tumor-infiltrating lymphocytes in surgically-resected biliary tract cancers.

BACKGROUND: Biliary tract cancers are rare, with a poor patient prognosis. Leptin and programmed death-ligand 1 (PD-L1) influence CD8+ and forkhead box P3 (FOXP3)+ lymphocytes, and thus, cancer cell growth. We aimed to define the prognostic implications of these variables and the clinicopathological features of biliary tract cancers. METHODS: Immunohistochemistry for leptin signaling-related proteins (leptin, leptin receptor, pSTAT3, extracellular-regulated kinase, mammalian target of rapamycin), PD-L1, CD8, and FOXP3 and in situ hybridization for Epstein-Barr virus-encoded small RNAs were performed in 147 cases of surgically-resected biliary tract cancers. RESULTS: Immune cell PD-L1-positivity, tumor size < 3 cm, adjuvant chemotherapy, no recurrence, and early-stage tumors were correlated with better 5-year survival in the tumoral PD-L1(-) and leptin(-) subgroups, and extrahepatic cholangiocarcinoma through multivariate analysis (all p < 0.05). Immune cell PD-L1 and adjuvant chemotherapy lost its prognostic significance in the tumoral PD-L1+ and leptin+ subgroups. CONCLUSIONS: The prognostic implication of the variables may depend upon tumoral protein expression and the anatomical site. Immune cell PD-L1-positivity and the administration of adjuvant chemotherapy may indicate the favorable survival of patients with surgically-resected biliary tract cancers, specifically, in the tumoral PD-L1(-) or tumor leptin(-) subgroups and extrahepatic cholangiocarcinoma. PD-L1- or leptin-targeted therapy combined with conventional chemotherapy may benefit the tumoral PD-L1+ or leptin+ subgroups.

Pan-cancer molecular tumor board experience with biomarker-driven precision immunotherapy.

Despite remarkable responses to immune checkpoint blockade (ICB) in some advanced cancers, most patients do not benefit, perhaps due to the complexity of tumor/immune/genome interactions. We implemented a multidisciplinary Molecular Tumor Board (MTB) that reviewed multi-omic cancer characteristics to develop N-of-One therapies for patients in the pan-cancer, advanced, refractory setting. This study evaluates the experience of 80 patients who were presented to the MTB and received a treatment regimen that included ICB. Overall, 60/80 patients (75%) who received ICB following MTB discussion had a high degree of matching between tumor molecular characteristics, including ICB biomarkers (reflected by a high Matching Score (≥50%)) and therapy administered. Patients with high versus low Matching Score experienced significantly longer median progression-free survival (6.4 vs. 3.0 months; p = 0.011) and median overall survival (15.3 vs. 4.7 months; p = 0.014) and higher clinical benefit rates (stable disease ≥6 months/partial response/complete response) (53% vs. 21%, p = 0.019). Although most patients (52/80 (65%)) received a personalized combination therapy (e.g., targeted, hormonal, chemotherapy, or a second immunotherapy agent), administering >1 drug was not associated with outcome. Only degree of matching and age, but no other variables, including individual biomarkers (e.g., microsatellite status, tumor mutational burden, or PD-L1 status), were independently correlated with outcome. In the pan-cancer setting, the MTB facilitated a precision medicine strategy to match therapeutic regimens that included ICB alone or combined with matched targeted drugs to patients with advanced malignancy, which was associated with improved clinical outcomes.

Corrigendum: Artificial Intelligence for Breast Cancer Screening in Mammography (AI-STREAM): A Prospective Multicenter Study Design in Korea Using AI-Based CADe/x.

This corrects the article on p. 57 in vol. 25, PMID: 35133093.

Improving the Performance of Radiologists Using Artificial Intelligence-Based Detection Support Software for Mammography: A Multi-Reader Study.

OBJECTIVE: To evaluate whether artificial intelligence (AI) for detecting breast cancer on mammography can improve the performance and time efficiency of radiologists reading mammograms. MATERIALS AND METHODS: A commercial deep learning-based software for mammography was validated using external data collected from 200 patients, 100 each with and without breast cancer (40 with benign lesions and 60 without lesions) from one hospital. Ten readers, including five breast specialist radiologists (BSRs) and five general radiologists (GRs), assessed all mammography images using a seven-point scale to rate the likelihood of malignancy in two sessions, with and without the aid of the AI-based software, and the reading time was automatically recorded using a web-based reporting system. Two reading sessions were conducted with a two-month washout period in between. Differences in the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and reading time between reading with and without AI were analyzed, accounting for data clustering by readers when indicated. RESULTS: The AUROC of the AI alone, BSR (average across five readers), and GR (average across five readers) groups was 0.915 (95% confidence interval, 0.876-0.954), 0.813 (0.756-0.870), and 0.684 (0.616-0.752), respectively. With AI assistance, the AUROC significantly increased to 0.884 (0.840-0.928) and 0.833 (0.779-0.887) in the BSR and GR groups, respectively (p = 0.007 and p < 0.001, respectively). Sensitivity was improved by AI assistance in both groups (74.6% vs. 88.6% in BSR, p < 0.001; 52.1% vs. 79.4% in GR, p < 0.001), but the specificity did not differ significantly (66.6% vs. 66.4% in BSR, p = 0.238; 70.8% vs. 70.0% in GR, p = 0.689). The average reading time pooled across readers was significantly decreased by AI assistance for BSRs (82.73 vs. 73.04 seconds, p < 0.001) but increased in GRs (35.44 vs. 42.52 seconds, p < 0.001). CONCLUSION: AI-based software improved the performance of radiologists regardless of their experience and affected the reading time.

Precision medicine-based therapies in advanced colorectal cancer: The University of California San Diego Molecular Tumor Board experience.

Treatment for advanced colorectal cancer is often limited by complex molecular profiles, which promote resistance to systemic agents and targeted monotherapies. Recent studies suggest that a personalized, combinatorial approach of matching drugs to tumor alterations may be more effective. We implemented a precision medicine strategy by forming a Molecular Tumor Board (MTB), a multidisciplinary team of clinicians, scientists, bioinformaticians and geneticists. The MTB integrated molecular profiling information and patient characteristics to develop N-of-One treatments for 51 patients with advanced colorectal cancer. All patients had metastatic disease and 63% had received ≥ 3 prior therapy lines. Overall, 34/51 patients (67%) were matched to ≥ 1 drug recommended by the MTB based on individual tumor characteristics, whereas 17/51 (33%) patients received unmatched therapies. Patients who received matched therapy demonstrated significantly longer progression-free survival (hazard ratio [HR], 0.41; 95% confidence interval [CI], 0.21-0.81; P = 0.01) and a trend towards higher clinical benefit rates (41% vs. 18%, P = 0.058) (all multivariate) compared to patients receiving unmatched therapy. The MTB facilitated personalized matching of drugs to tumor characteristics, which was associated with improved progression-free survival in patients with advanced colorectal cancer.

Case series of outcomes in advanced cancer patients with single pathway alterations receiving N-of-One therapies.

Though advanced cancers generally display complex molecular portfolios, there is a subset of patients whose malignancies possess only one genomic alteration or alterations in one oncogenic pathway. We assess how N-of-One therapeutic strategies impact outcomes in these patients. From 12/2012 to 9/2018, 429 therapy-evaluable patients with diverse treatment-refractory cancers were presented at Molecular Tumor Boards at Moores Cancer Center at UC San Diego. The clinical benefit rate, defined by RECIST1.1, was assessed for patients with solid tumors who underwent next-generation sequencing (NGS) profiling revealing one genomic or pathway alteration, subsequently managed with N-of-One therapies. Nine of 429 patients (2.1%) met evaluation criteria. Using matched therapy indicated by NGS, the clinical benefit rate (stable disease ≥ 6 months/partial/complete response) was 66.7%. Median progression-free survival was 11.3 months (95% CI: 3.4-not evaluable). Thus, a small subset of diverse cancers has single pathway alterations on NGS testing. These patients may benefit from customized therapeutic matching.

Mammographically occult breast cancers detected with AI-based diagnosis supporting software: clinical and histopathologic characteristics.

BACKGROUND: To demonstrate the value of an artificial intelligence (AI) software in the detection of mammographically occult breast cancers and to determine the clinicopathologic patterns of the cancers additionally detected using the AI software. METHODS: By retrospectively reviewing our institutional database (January 2017-September 2019), we identified women with mammographically occult breast cancers and analyzed their mammography with an AI software that provided a malignancy score (range 0-100; > 10 considered as positive). The hot spots in the AI report were compared with the US and MRI findings to determine if the cancers were correctly marked by the AI software. The clinicopathologic characteristics of the AI-detected cancers were analyzed and compared with those of undetected cancers. RESULTS: Among the 1890 breast cancers, 6.8% (128/1890) were mammographically occult, among which 38.3% (49/128) had positive results in the AI analysis. Of them, 81.6% (40/49) were correctly marked by the AI software and determined as "AI-detected cancers." As such, 31.3% (40/128) of mammographically occult breast cancers could be identified by the AI software. Of the AI-detected cancers, 97.5% were found in heterogeneously or extremely dense breasts, 52.5% were asymptomatic, 86.5% were invasive, and 29.7% had axillary lymph node metastasis. Compared with undetected cancers, the AI-detected cancers were more likely to be found in younger patients (p < 0.001), undergo neoadjuvant chemotherapy as well as mastectomy rather than breast-conserving operation (both p < 0.001), and accompany axillary lymph node metastasis (p = 0.003). CONCLUSIONS: AI conferred an added value in the detection of mammographically occult breast cancers.

Enhanced proton treatment with a LDLR-ligand peptide-conjugated gold nanoparticles targeting the tumor microenvironment in an infiltrative brain tumor model.

The tumor microenvironment (TME) of glioblastoma malforms (GBMs) contains tumor invasiveness factors, microvascular proliferation, migratory cancer stem cells and infiltrative tumor cells, which leads to tumor recurrence in the absence of effective drug delivery in a Blood Brain Barrier (BBB)-intact TME and radiological invisibility. Low-density lipoprotein receptor (LDLR) is abundant in the blood brain barrier and overexpressed in malignant glioma cells. This study aimed to treat the TME with transmitted proton sensitization of LDLR ligand-functionalized gold nanoparticles (ApoB@AuNPs) in an infiltrative F98 glioma rat model. BBB-crossing ApoB@AuNPs were selectively taken up in microvascular endothelial cells proliferation and pericyte invasion, which are therapeutic targets in the glioma TME. Proton sensitization treated the TME and bulk tumor volume with enhanced therapeutic efficacy by 67-75% compared to that with protons alone. Immunohistochemistry demonstrated efficient treatment of endothelial cell proliferation and migratory tumor cells of invasive microvessels in the TME with saving normal tissues. Taken together, these data indicate that the use of LDLR ligand-functionalized gold nanoparticles is a promising strategy to treat infiltrative malignant glioma while overcoming BBB crossing.

Artificial Intelligence for Breast Cancer Screening in Mammography (AI-STREAM): A Prospective Multicenter Study Design in Korea Using AI-Based CADe/x.

PURPOSE: Artificial intelligence (AI)-based computer-aided detection/diagnosis (CADe/x) has helped improve radiologists' performance and provides results equivalent or superior to those of radiologists' alone. This prospective multicenter cohort study aims to generate real-world evidence on the overall benefits and disadvantages of using AI-based CADe/x for breast cancer detection in a population-based breast cancer screening program comprising Korean women aged ≥ 40 years. The purpose of this report is to compare the diagnostic accuracy of radiologists with and without the use of AI-based CADe/x in mammography readings for breast cancer screening of Korean women with average breast cancer risk. METHODS: Approximately 32,714 participants will be enrolled between February 2021 and December 2022 at 5 study sites in Korea. A radiologist specializing in breast imaging will interpret the mammography readings with or without the use of AI-based CADe/x. If recall is required, further diagnostic workup will be conducted to confirm the cancer detected on screening. The findings will be recorded for all participants regardless of their screening status to identify study participants with breast cancer diagnosis within both 1 year and 2 years of screening. The national cancer registry database will be reviewed in 2026 and 2027, and the results of this study are expected to be published in 2027. In addition, the diagnostic accuracy of general radiologists and radiologists specializing in breast imaging from another hospital with or without the use of AI-based CADe/x will be compared considering mammography readings for breast cancer screening. DISCUSSION: The Artificial Intelligence for Breast Cancer Screening in Mammography (AI-STREAM) study is a prospective multicenter study that aims to compare the diagnostic accuracy of radiologists with and without the use of AI-based CADe/x in mammography readings for breast cancer screening of women with average breast cancer risk. AI-STREAM is currently in the patient enrollment phase. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05024591.

FBI-1 inhibits epithelial-to-mesenchymal transition, migration, and invasion in lung adenocarcinoma A549 cells by downregulating transforming growth factor-ß1 signaling pathway.

The factor binding inducer of short transcripts-1 (FBI-1) is a POZ-domain Kruppel-like (POK) family of transcription factors and is known as a proto-oncogene or tumor suppressor in various carcinomas. However, the role of FBI-1 on epithelial-to-mesenchymal transition (EMT) and invasiveness in lung cancer remains unknown. Preliminarily, clinical data such as tissue microarray, Kaplan-Meier, and Oncomine were analyzed to confirm the correlation between lung cancer metastasis and FBI-1. To investigate the function of FBI-1 in EMT in lung cancer, EMT was measured in FBI-1-deficient or FBI-1-overexpressing cells. FBI-1 showed decreased expression in tumors metastasized to lymph nodes than in the primary tumor. In addition, it was also associated with improved survival rates of lung cancer patients. FBI-1 knockdown improved E-to-N-cadherin switching, migration, and invasion in A549 cells, similar to the initiation of EMT stimulated by transforming growth factor- ß1 (TGF-ß1). In contrast, overexpression of FBI-1 inhibited the transcription and activation of Smad2, thereby interfering with EMT, despite stimulation by TGF-ß1. These results suggest that FBI-1 plays a negative role in EMT in lung cancer via the TGF-ß1 signaling pathway, implying its use as a new potential therapeutic target and diagnostic indicator for early stage of lung cancer metastasis.

Real-World Data From a Molecular Tumor Board: Improved Outcomes in Breast and Gynecologic Cancers Patients With Precision Medicine.

PURPOSE: Next-generation sequencing is increasingly used in gynecologic and breast cancers. Multidisciplinary Molecular Tumor Board (MTB) may guide matched therapy; however, outcome data are limited. We evaluate the effect of the degree of matching of tumors to treatment as well as compliance to MTB recommendations on outcomes. METHODS: Overall, 164 patients with consecutive gynecologic and breast cancers presented at MTB were assessed for clinicopathologic data, next-generation sequencing results, MTB recommendations, therapy received, and outcomes. Matching score (MS), defined as percentage of alterations targeted by treatment over total pathogenic alterations, and compliance to MTB recommendations were analyzed in context of oncologic outcomes. RESULTS: Altogether, 113 women were evaluable for treatment after MTB; 54% received matched therapy. Patients with MS ≥ 40% had higher overall response rate (30.8% v 7.1%; P = .001), progression-free survival (PFS; hazard ratio [HR] 0.51; 95% CI, 0.31 to 0.85; P = .002), and a trend toward improved overall survival (HR 0.64; 95% CI, 0.34 to 1.25; P = .082) in univariate analysis. The PFS advantage remained significant in multivariate analysis (HR 0.5; 95% CI, 0.3 to 0.8; P = .006). Higher MTB recommendation compliance was significantly associated with improved median PFS (9.0 months for complete; 6.0 months for partial; 4.0 months for no compliance; P = .004) and overall survival (17.1 months complete; 17.8 months partial; 10.8 months none; P = .046). Completely MTB-compliant patients had higher MS (P < .001). In multivariate analysis comparing all versus none MTB compliance, overall response (HR 9.5; 95% CI, 2.6 to 35.0; P = .001) and clinical benefit (HR 8.8; 95% CI, 2.4 to 33.2; P = .001) rates were significantly improved with higher compliance. CONCLUSION: Compliance to MTB recommendations resulted in higher degrees of matched therapy and correlates with improved outcomes in patients with gynecologic and breast cancers.

The Impact of EGFR Tyrosine Kinase Inhibitor on the Natural Course of Concurrent Subsolid Nodules in Patients with Non-Small Cell Lung Cancer.

PURPOSE: The role of epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) in the management of persistent subsolid nodules (SSNs) is unclear. This study aimed to investigate the impact of EGFR-TKIs on concurrent SSNs in patients with stage IV non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: Patients who received an EGFR-TKI for at least 1 month for stage IV NSCLC and had concurrent SSN(s) that had existed for at least 3 months on chest computed tomography were included in this retrospective study. Size change of each nodule before and after EGFR-TKI therapies were evaluated using a cutoff value of 2 mm; increase (≥ 2 mm), decrease (≤ -2 mm), and no change (-2 mm < size change < +2 mm). RESULTS: A total of 77 SSNs, 51 pure ground-glass (66.2%) and 26 part-solid nodules (33.8%), were identified in 59 patients who received gefitinib (n=45) and erlotinib (n=14). Among 58 EGFR mutation analysis performed for primary lung cancer, 45 (77.6%) were EGFR mutant. The proportions of decrease group were 19.5% (15/77) on per-nodule basis and 25.4% (15/59) on per-patient basis. Four SSNs (5.2%) disappeared completely. On per-patient based multivariable analysis, EGFR exon 19 deletion positivity for primary lung cancer was associated with a decrease after initial EGFR-TKI therapy (adjusted odds ratio, 4.29; 95% confidence interval, 1.21 to 15.29; p=0.025). CONCLUSION: Approximately 20% of the concurrent SSNs decreased after the initial EGFR-TKI therapy. EGFR exon 19 deletion positivity for primary lung cancer was significantly associated with the size change of concurrent SSNs.