Prediction of breast cancer and lymph node metastatic status with tumour markers using logistic regression models

2008 ◽  
Vol 14 (2) ◽  
pp. 275-280 ◽  
Author(s):  
Hsiao-Lin Hwa ◽  
Wen-Hong Kuo ◽  
Li-Yun Chang ◽  
Ming-Yang Wang ◽  
Tao-Hsin Tung ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Lymph Node ◽  
Regression Models ◽  
Tumour Markers ◽  
Logistic Regression Models

scholarly journals Deep Learning Generates Custom-made Logistic Regression Models for Explaining how Breast Cancer Subtypes are Classified

2021 ◽  
Author(s):  
Takuma Shibahara ◽  
Chisa Wada ◽  
Yasuho Yamashita ◽  
Kazuhiro Fujita ◽  
Masamichi Sato ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Deep Learning ◽  
Linear Model ◽  
Regression Models ◽  
Learning Model ◽  
Logistic Regression Models ◽  
Custom Made ◽  
Meta Learning ◽  
Deep Learning Model

Abstract Breast cancer is the most frequently found cancer in women and the one most often subjected to genetic analysis. Nonetheless, it has been causing the largest number of women's cancer-related deaths. PAM50, the intrinsic subtype assay for breast cancer, is beneficial for diagnosis but does not explain each subtype’s mechanism. Deep learning can predict the subtypes from genetic information more accurately than conventional statistical methods. However, the previous studies did not directly use deep learning to examine which genes associate with the subtypes. To reveal the mechanisms embedded in the PAM50 subtypes, we developed an explainable deep learning model called a point-wise linear model, which uses meta-learning to generate a custom-made logistic regression for each sample. We developed an explainable deep learning model called a point-wise linear model, which uses meta-learning to generate a custom-made logistic regression for each sample. Logistic regression is familiar to physicians, and we can use it to analyze which genes are important for prediction. The custom-made logistic regression models generated by the point-wise linear model used the specific genes selected in other subtypes compared to the conventional logistic regression model: the overlap ratio is less than twenty percent. Analyzing the point-wise linear model’s inner state, we found that the point-wise linear model used genes relevant to the cell cycle-related pathways.

scholarly journals Deep Inguinal Lymph Node Metastases Can Predict Pelvic Lymph Node Metastases and Prognosis in Penile Squamous Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhenyu Yang ◽  
Xingliang Tan ◽  
Yanjun Wang ◽  
Yuantao Zou ◽  
Dong Chen ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Logistic Regression ◽  
Lymph Node ◽  
Regression Models ◽  
Inguinal Lymph Node ◽  
Pelvic Lymph Node ◽  
Logistic Regression Models ◽  
Node Metastasis ◽  
Node Metastases ◽  
Penile Squamous Cell Carcinoma

ObjectivesTo evaluate the relationship between deep inguinal lymph node metastasis (ILNM) and pelvic lymph node metastasis (PLNM) and explore the prognostic value of deep ILNM in penile squamous cell carcinoma (PSCC).Materials and MethodsThe records of 189 patients with ILNM treated for PSCC were analysed retrospectively. Logistic regression models were used to test for predictors of PLNM. Cox regression was performed in univariable and multivariable analyses of cancer-specific survival (CSS). CSS was compared using Kaplan-Meier analyses and log rank tests.ResultsPLNM were observed in 53 cases (28.0%). According to logistic regression models, only deep ILNM (OR 9.72, p<0.001) and number (≥3) of metastatic inguinal lymph nodes (ILNs) (OR 2.36, p=0.03) were independent predictors of PLNM. The incidences of PLNM were 18% and 19% with negative deep ILNM and extranodal extension (ENE); and 76% and 42% with positive deep ILNM and ENE, respectively. The accuracy of deep ILNM, ENE, bilateral involvement and number (≥3) of ILNMs for predicting PLNM were 81.0%, 65.6%, 63.5% and 67.2%, respectively. The CSS was significantly different in patients with positive and negative deep ILNM (median 1.7 years vs not reached, p<0.01). Patients who presented with deep ILNM had worse CSS (median 3.8 years vs not reached, p<0.01) in those with negative PLNs.ConclusionsDeep ILNM is the most accurate factor for predicting PLNM in PSCC according to our data. We recommend that patients with deep ILNM should be referred for pelvic lymph node dissection. Involvement of deep ILNs indicates poor prognosis. We propose that patients with metastases of deep ILNs may be staged as pN3.

scholarly journals Deep learning generates custom-made logistic regression models for explaining how breast cancer subtypes are classified

2021 ◽  
Author(s):  
Takuma Shibahara ◽  
Chisa Wada ◽  
Yasuho Yamashita ◽  
Kazuhiro Fujita ◽  
Masamichi Sato ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Deep Learning ◽  
Regression Model ◽  
Linear Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Learning Approach ◽  
Logistic Regression Models ◽  
Custom Made

Breast cancer is the most frequently found cancer in women and the one most often subjected to genetic analysis. Nonetheless, it has been causing the largest number of women's cancer-related deaths. PAM50, the intrinsic subtype assay for breast cancer, is beneficial for diagnosis and stratified treatment but does not explain each subtype's mechanism. Nowadays, deep learning can predict the subtypes from genetic information more accurately than conventional statistical methods. However, the previous studies did not directly use deep learning to examine which genes associate with the subtypes. Ours is the first study on a deep-learning approach to reveal the mechanisms embedded in the PAM50-classified subtypes. We developed an explainable deep learning model called a point-wise linear model, which uses a meta-learning approach to generate a custom-made logistic regression model for each sample. Logistic regression is familiar to physicians and medical informatics researchers, and we can use it to analyze which genes are important for subtype prediction. The custom-made logistic regression models generated by the point-wise linear model for each subtype used the specific genes selected in other subtypes compared to the conventional logistic regression model: the overlap ratio is less than twenty percent. And analyzing the point-wise linear model's inner state, we found that the point-wise linear model used genes relevant to the cell cycle-related pathways. The results of this study suggest the potential of our explainable deep learning to play a vital role in cancer treatment.

The impact of SPY angiography on intraoperative decision making and outcomes for post-mastectomy reconstruction

2019 ◽  
pp. 1-5
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Skin Necrosis ◽  
Regression Models ◽  
Multivariable Analysis ◽  
P Value ◽  
Flap Necrosis ◽  
Single Institution ◽  
Logistic Regression Models ◽  
The Impact

Objective: While the use of intraoperative laser angiography (SPY) is increasing in mastectomy patients, its impact in the operating room to change the type of reconstruction performed has not been well described. The purpose of this study is to investigate whether SPY angiography influences post-mastectomy reconstruction decisions and outcomes. Methods and materials: A retrospective analysis of mastectomy patients with reconstruction at a single institution was performed from 2015-2017.All patients underwent intraoperative SPY after mastectomy but prior to reconstruction. SPY results were defined as ‘good’, ‘questionable’, ‘bad’, or ‘had skin excised’. Complications within 60 days of surgery were compared between those whose SPY results did not change the type of reconstruction done versus those who did. Preoperative and intraoperative variables were entered into multivariable logistic regression models if significant at the univariate level. A p-value <0.05 was considered significant. Results: 267 mastectomies were identified, 42 underwent a change in the type of planned reconstruction due to intraoperative SPY results. Of the 42 breasts that underwent a change in reconstruction, 6 had a ‘good’ SPY result, 10 ‘questionable’, 25 ‘bad’, and 2 ‘had areas excised’ (p<0.01). After multivariable analysis, predictors of skin necrosis included patients with ‘questionable’ SPY results (p<0.01, OR: 8.1, 95%CI: 2.06 – 32.2) and smokers (p<0.01, OR:5.7, 95%CI: 1.5 – 21.2). Predictors of any complication included a change in reconstruction (p<0.05, OR:4.5, 95%CI: 1.4-14.9) and ‘questionable’ SPY result (p<0.01, OR: 4.4, 95%CI: 1.6-14.9). Conclusion: SPY angiography results strongly influence intraoperative surgical decisions regarding the type of reconstruction performed. Patients most at risk for flap necrosis and complication post-mastectomy are those with questionable SPY results.

scholarly journals Effect of prostatic apex shape (Lee types) and urethral sphincter length in preoperative MRI on very early continence rates after radical prostatectomy

2021 ◽  
Author(s):  
Mike Wenzel ◽  
Felix Preisser ◽  
Matthias Mueller ◽  
Lena H. Theissen ◽  
Maria N. Welte ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Radical Prostatectomy ◽  
Regression Models ◽  
Urethral Sphincter ◽  
Anatomical Characteristics ◽  
Logistic Regression Models ◽  
Pad Test ◽  
Type D ◽  
Multivariable Logistic Regression ◽  
Type C

Abstract Purpose To test the effect of anatomic variants of the prostatic apex overlapping the membranous urethra (Lee type classification), as well as median urethral sphincter length (USL) in preoperative multiparametric magnetic resonance imaging (mpMRI) on the very early continence in open (ORP) and robotic-assisted radical prostatectomy (RARP) patients. Methods In 128 consecutive patients (01/2018–12/2019), USL and the prostatic apex classified according to Lee types A–D in mpMRI prior to ORP or RARP were retrospectively analyzed. Uni- and multivariable logistic regression models were used to identify anatomic characteristics for very early continence rates, defined as urine loss of ≤ 1 g in the PAD-test. Results Of 128 patients with mpMRI prior to surgery, 76 (59.4%) underwent RARP vs. 52 (40.6%) ORP. In total, median USL was 15, 15 and 10 mm in the sagittal, coronal and axial dimensions. After stratification according to very early continence in the PAD-test (≤ 1 g vs. > 1 g), continent patients had significantly more frequently Lee type D (71.4 vs. 54.4%) and C (14.3 vs. 7.6%, p = 0.03). In multivariable logistic regression models, the sagittal median USL (odds ratio [OR] 1.03) and Lee type C (OR: 7.0) and D (OR: 4.9) were independent predictors for achieving very early continence in the PAD-test. Conclusion Patients’ individual anatomical characteristics in mpMRI prior to radical prostatectomy can be used to predict very early continence. Lee type C and D suggest being the most favorable anatomical characteristics. Moreover, longer sagittal median USL in mpMRI seems to improve very early continence rates.

785 Reported Restful Sleep Predicting Emotional Distress: Does Exercise (and its modalities) moderate?

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A305-A306
Author(s):  
Jesse Moore ◽  
Ellita Williams ◽  
Collin Popp ◽  
Anthony Briggs ◽  
Judite Blanc ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Aerobic Exercise ◽  
Regression Models ◽  
Regression Analyses ◽  
Logistic Regression Models ◽  
The Past ◽  
Strengthening Exercise ◽  
Waking Up ◽  
The Relationship ◽  
Age And Sex

Abstract Introduction Literature shows that exercise moderates the relationship between sleep and emotional distress (ED.) However, it is unclear whether different types of exercise, such as aerobic and strengthening, affect this relationship differently. We investigated the moderating role of two types of exercise (aerobic and strengthening) regarding the relationship between ED and sleep. Methods Our analysis was based on data from 2018 National Health Interview Survey (NHIS), a nationally representative study in which 2,814 participants provided all data. Participants were asked 1) “how many days they woke up feeling rested over the past week”, 2) the Kessler 6 scale to determine ED (a score &gt;13 indicates ED), and 3) the average frequency of strengthening or aerobic exercise per week. Logistic regression analyses were performed to determine if the reported days of waking up rested predicted level of ED. We then investigated whether strengthening or aerobic exercise differentially moderated this relationship. Covariates such as age and sex were adjusted in the logistic regression models. Logistic regression analyses were performed to determine if subjective reporting of restful sleep predicted level of ED. We investigated whether strengthening exercise or aerobic exercise differentially moderated this relationship. Covariates such as age and sex were adjusted in the logistic regression models. Results On average, participants reported 4.41 restful nights of sleep (SD =2.41), 3.43 strengthening activities (SD = 3.19,) and 8.47 aerobic activities a week (SD=5.91.) We found a significant association between days over the past week reporting waking up feeling rested and ED outcome according to K6, Χ2(1) = -741, p= &lt;.001. The odds ratio signified a decrease of 52% in ED scores for each unit of restful sleep (OR = .48, (95% CI = .33, .65) p=&lt;.001.) In the logistic regression model with moderation, aerobic exercise had a significant moderation effect, Χ2(1) = .03, p=.04, but strengthening exercise did not. Conclusion We found that restful sleep predicted reduction in ED scores. Aerobic exercise moderated this relationship, while strengthening exercise did not. Further research should investigate the longitudinal effects of exercise type on the relationship between restful sleep and ED. Support (if any) NIH (K07AG052685, R01MD007716, K01HL135452, R01HL152453)

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