Our data suggest that AR may provide another specific definition of breast cancer subtypes and reveal a potential role in DCIS progression. These findings may help develop new therapies.
Breast cancer is considered as one of the multifactorial diseases. The aim of the current study is to investigate the association between P-cadherin and molecular subtypes of breast cancer, especially the basal-like subtype. Two hundred and thirteen breast-invasive ductal carcinomas were involved in this study. The expressions of P-cadherin were detected via immunohistochemistry. The 213 cases were divided into luminal A, luminal B, HER2 overexpression subtype, and normal breast-like and basal-like subtypes according to the standard of molecular breast cancer subtypes. In addition, the expressions of CK5/6 and CK14 were detected to distinguish between the normal breast-like and the basal-like subtypes. P-cadherin expression was found in 91 cases of 213 breast-invasive ductal carcinomas, with a positive rate of 42.7%. P-cadherin correlated negatively with estrogen receptor (ER) (p=0.001) and progesterone receptor (p=0.001), whereas it positively correlated with histologic grade (p=0.003), NPI (p=0.005), p53 (p=0.038), and Ki67 (p=0.022). P-cadherin expression showed a strong correlation with recurrence and distant metastasis (p=0.009), and invasion of the vascular and soft tissues (p=0.004). Moreover, P-cadherin expression existed in the basal-like and non-basal-like subtypes. During prognosis, P-cadherin expression was associated with decreased disease-free survival in patients (p=0.009) and overall survival (OS) (p=0.005). In addition, multivariate analysis showed that tumor grade (p=0.021), ER (p=0.015), clinical stage (p=0.001), and P-cadherin (p=0.033) were significant predictors of OS. The current data suggest that P-cadherin may be used to distinguish the basal-like subtype and to predict the outcome in view of the relationship with DFS and OS. Furthermore, P-cadherin expression may be useful in making treatment decisions.
The goal of this project is to train unsupervised machine learning (ML) models to cluster patients into different groups based on their individual features (such as morphology, cancer stage, treatment intent, site location, etc.). Then, we use the distribution of the prescription -Rx (number of fractions times dose per fraction) within each cluster to establish typical Rx values as well as less frequent values. In clinical setting, a new patient's Rx can then be flagged if it does not fall into the typical range of Rx for the individual patient's feature-set. Additionally, the value of Rx is predicted with supervised ML models. This AI computed value can be compared against the clinical Rx to create an additional flag if there is a significant relative deviation. We aim to implement an automatic mechanism based on this ML model along with visualization tools to assist peer review during weekly chart rounds in order to provide extra safety for patients by flagging the infrequent used Rx. Materials/Methods: We queried all radiation oncology patients from the electronic patient information management system treated between 01/01/ 2007 to 01/01/2021 (14 years of retrospective data) at our institution. Based on their diagnostic code (ICD9/ICD10 code), we categorize the patients into different disease groups (i.e., prostate, H&N, CNS patients and etc.) for various disease specific model training. Several clustering models (k-means, hierarchical) were applied to cluster patients into different groups based on their individual features, which includes morphology code, treatment intent, treatment techniques, treatment type (initial or cone down), TNM stage, treatment site and etc.). Then, supervised ML models (Random Forest Regression) were applied to predict each group's Rx distribution, which gives an estimate of the most frequent and least frequent Rx range within the group. A data pipeline was developed and then applied to prostate patients for initial testing. Results: We applied agglomerative clustering to the prostate group and found natural partitions of the patients into k = 2-5 clusters. Observation of the Rx in different clusters showed that each cluster had distinct characteristic Rx's. We plotted the Rx distribution for each feature, the scatterplot of Rx against each feature, and Rx distribution for selected feature-sets for visualization. The supervised prediction results are pending. Conclusion: This initial prostate ML model along with visualization tools can flag the Rx that are atypical for a particular cluster for further investigation in order to improve the efficiency of peer review process. This further investigation could result in detecting anomaly Rx therefore providing extra safety for patients.
Volume 32 -Issue S7 -2021 S1445Conclusions: These results above showed this regimen was feasible and tolerable for surgical conversion of stage IV GC pts. Sintilimab, doublet chemotherapy, and apatinib might offer an opportunity of life-prolonging or cure for this population. Trial ID: NCT04267549.
LCNEC is an aggressive subtype of non-smallcell lung cancer (NSCLC), with a biology and prognosis comparable to small cell lung cancer (SCLC). The optimal treatment strategy is unknown. Prophylactic cranial irradiation (PCI) is known to reduce the incidence of brain metastases and improve survival in patients with SCLC. The purpose of this study is to determine the incidence of brain metastases and survival outcomes after treatment of LCNEC with curative intent. Materials/Methods: Retrospective chart review was conducted on patients in Nova Scotia diagnosed with primary LCNEC of the lung between September 1998 and February 2014. Information was collected from patient charts including tumor staging, treatment intent and type, brain metastases, and dates of relapse and death (if applicable). The primary endpoint was the incidence of brain metastases in patients treated with curative intent. The secondary endpoints included overall survival (OS), disease free survival (DFS), and the incidence of brain metastases as the first site of relapse. Results: 87 patients were eligible. 53 patients were treated with curative intent, and 34 with palliative intent. Median follow-up time was 16.9 months (IQ range 11.4-30.2 months) for patients treated with curative intent and 8.1 months (IQ range 2.5-13.9 months) for those treated with palliative intent. The most common therapy for patients treated with curative intent was surgery alone (34 patients, 64.1%). Ten patients received surgery with adjuvant therapy (5 receiving chemotherapy and 5 receiving concurrent chemoradiation therapy), 6 received definitive concurrent chemoradiation, and one received radiation therapy alone. For patients treated with curative intent, the incidence of brain metastases was 20.6% at 1 year and 42.5% at 2 years. Of 19 patients experiencing brain relapse, 15 developed brain metastases either as an isolated first site of relapse or as part of the first relapse (with other sites). The median OS and DFS in patients treated with curative intent were 19.1 months and 13.3 months. Considering all patients, the incidence of brain metastases was 23.4% at 1 year and 44.8% at 2 years. Conclusion: For patients treated for LCNEC of the lung with curative intent, the incidence of brain metastases approaches that noted in SCLC. PCI should be investigated further as a means of preventing brain metastases.
The median GTV at about 30 days after the beginning of X-irradiation compared with that before treatment was 70.2% (48.8-92.7, CDDP and S-1) and 72.3% (28.8-92.6, CDDP and vinorelbine), respectively. At 30 days, the shrinkage rates of GTV after CCPT were significantly higher than those after CCRT using either regimen of chemotherapy. Conclusion: CCPT using CDDP and S-1 is considered to be very effective in obtaining a favorable primary response. The treatment appeared to have a strong tumor-shrinking effect during an early period. Frequent verification and adaptive plan making according to a verification plan are considered necessary. Further investigation with more patients and more detailed evaluation seems to be warranted.
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