The aim of this study is to report the clinicopathologic and outcome features of superficial high-grade and deep low-grade penile squamous cell carcinomas. For this, we used a retrospectively-colleted series of patients with penile cancer, in which we identified 41 cases corresponding to 12 superficial high-grade tumors and 29 deep low-grade tumors. As outcomes we evaluated inguinal lymph node status, presence of tumor relapse, final nodal status, and cancer-specific death. Follow-up ranged from 0.8 to 386.7 months (mean, 152.5 months; median, 157.3 months). Clinicopathologic features were similar between superficial high-grade and deep low-grade tumors, except for a tendendy (Fisher’s exact P = 0.057) of the former to include tumors with a verruciform pattern of growth. A significantly higher proportion of inguinal lymph node metastasis was found in superficial high-grade tumors compared to deep low-grade tumors (80% vs. 20% respectively, Fisher’s exact P = 0.02). No significant differences were found regarding tumor relapse (Fisher’s exact P = 0.52), final nodal status (Mantel-Cox’s P = 0.42), or cancer-related death (Mantel-Cox’s P = 0.52). Our findings suggest that patients with superficial high-grade tumors may be treated differently from patients with deep low-grade tumors, at least to control short-term local disease. Prophylactic inguinal lymphadenectomuy might be indicated in cases of superficial tumors with high-grade histology while in deeply invasive low-grade penile carcinomas a more conservative approach may be considered.
This repository contains the full statistical analysis of the dataset that was used for the article "Clinicopathologic and Outcome Features of Superficial High-Grade and Deep Low-Grade Squamous Cell Carcinomas of the Penis". The article is published in SpringerPlus and is available here.
This repository also contains the following files:
- The preprint (post accepted) version of the article
- The BibTeX file containing all the references cited in the article
- The R script that was used for analyzing the dataset and write the article
- The R Markdown file used for this report
- The figures included in this repository in PNG format
Data were analyzed using R version 3.1.2 “Pumpkin Helmet” (R Foundation for Statistical Computing, Vienna, Austria). Results were written using RMarkDown inside RStudio version 0.99.292, powered by the knitr package version 1.9 by Yihui Xie.
First we loaded the full dataset including 333 patiens with invasive penile squamous cell carcinoma.
Data <- read.csv("Article/PenisSCC_333.csv")The full dataset is available at http://dx.doi.org/10.6084/m9.figshare.1290997, which also contains the dataset's codebook. We excluded patients who were lost at follow up in Outcome.
Data <- subset(Data, Outcome != "Lost at follow-up")We then recoded 3 variables, creating new variables cancer-related death (DOD), final nodal status (Final_Nodal), and anatomical location (Anatomical). Positive cases for Final_Nodalwhere those with lymph node metastasis in the groin dissection, local relapse during follow-up, or unfavorable outcome, including alive with disease and death by cancer. We also reordered Subtype.
# Creating a new variable for cancer-related death
Data$DOD <- ifelse(Data$Outcome == "Died of cancer", c("Yes"), c("No"))
Data$DOD <- factor(Data$DOD)
# Creating a new variable for the final nodal status
## Positive cases where those with:
# Nodal metastasis in lymphadenectomy
Data$Final_Nodal <- ifelse(Data$Mets == "Yes", c("Positive"), c("Negative"))
# Local relapse during follow-up
Data$Final_Nodal[Data$Local == "Yes"] <- "Positive"
# Unfavorable outcome, including alive with disease and death by cancer
Data$Final_Nodal[Data$Outcome == "Alive with disease"] <- "Positive"
Data$Final_Nodal[Data$Outcome == "Died of cancer"] <- "Positive"
Data$Final_Nodal <- factor(Data$Final_Nodal)
# Creating a new variable for anatomical location
Data$Anatomical[Data$Glans == "Yes" &
Data$Sulcus == "No" &
Data$Foreskin == "No"] <- "Glans alone"
Data$Anatomical[Data$Glans == "Yes" &
Data$Sulcus == "Yes" &
Data$Foreskin == "No"] <- "Glans + Coronal sulcus"
Data$Anatomical[Data$Glans == "Yes" &
Data$Sulcus == "Yes" &
Data$Foreskin == "Yes"] <- "Glans + Coronal sulcus + Foreskin"
Data$Anatomical <- factor(Data$Anatomical,
levels = c("Glans alone", "Glans + Coronal sulcus",
"Glans + Coronal sulcus + Foreskin"))
# Releveling variables
Data$Subtype <- factor(Data$Subtype,
levels = c("Usual", "Verrucous", "Papillary", "Warty", "Mixed"))We finally searched the dataset for cases of superficial high-grade and deep low-grade tumors, using the following criteria:
- Superficial High-Grade Tumors: Grade 3 tumors invading lamina propria or superficial corpus spongiosum (tumor thickness equals to or less than 5 mm).
- Deep Low-Grade Tumors: Grade 1 tumors invading deep corpus spongiosum (tumor thickness equals to or greater than 10 mm) or corpus cavernosum, including tunica albuginea.
We then excluded the missing cases and dropped unused levels.
# Superficial High-Grade Tumors
Data$Paradoxical[Data$Grade == "Grade 3" &
Data$Level == "Lamina propria"] <- "Superficial High-Grade"
Data$Paradoxical[Data$Grade == "Grade 3" &
Data$Level == "Corpus spongiosum" &
Data$Thickness <= 5] <- "Superficial High-Grade"
# Deep Low-Grade Tumors
Data$Paradoxical[Data$Grade == "Grade 1" &
Data$Level == "Corpus cavernosum"] <- "Deep Low-Grade"
Data$Paradoxical[Data$Grade == "Grade 1" &
Data$Level == "Corpus spongiosum" &
Data$Thickness >= 10] <- "Deep Low-Grade"
# Converting to Factor
Data$Paradoxical <- factor(Data$Paradoxical,
levels = c("Superficial High-Grade", "Deep Low-Grade"))
# Excluding missing cases
Data <- Data[complete.cases(Data$Paradoxical), ]
# Dropping unused levels
Data <- droplevels(Data)Data analysis was carried out on this dataset, using the simpleR package.
library(simpleR)Data analysis is divided in 4 sections, as it follows:
Descriptive Statistics. All the variables included in the dataset were analyzed using bar plots, histograms, box plots, and one-way tables. Factor variables were described using absolute and relative percentages. Numeric variables were described using mean, standard deviation, median, interquartile range, minimum and maximum value.
Inferential Statistics: Statistical tests (Fisher's exact test for categorical variables, Kruskal-Wallis test for numerical variables) were carried out to compare the clinicopathologic and outcome features between superficial high-grade and deep low-grade tumors. A 2-tailed P value was reported in all instances. Reported statistics included absolute and relative percentages for categorical variables; and mean, standard deviation, median, interquartile range, minimum and maximum value for numeric variables, by tumor type (superficial high-grade vs. deep low-grade).
Survival Analysis. For all variables in the dataset survival curves were built for final nodal status and cancer-related death using the Kaplan-Meier method. Survival curves were compared using the Mantel-Cox (log-rank) test. Numerical variables were splitted in 2 levels using the median as the cutoff point. A 2-tailed P value was reported in all instances.
Logistic Regression Analysis. Odds ratios (OR) with 95% confidence intervals (CI) and their associated P values were estimated for superficial high-grade vs. deep low-grade tumors, considering inguinal lymph node metastasis, tumor relapse, final nodal status, and cancer-related death.
Cox's Proportional Hazards Regression Analysis. Hazard ratios (OR) with 95% confidence intervals (CI) and their associated P values were estimated for superficial high-grade vs. deep low-grade tumors, considering inguinal lymph node metastasis, tumor relapse, final nodal status, and cancer-related death.
library(knitr)
opts_chunk$set(fig.width = 8, fig.height = 6, message = FALSE, warning = FALSE)Here it follows the description of all the variables included in the analyzed dataset.
Var <- Data$Procedure
categorical.plot(Var)
descriptive.categorical(Var)| No. Cases | % | |
|---|---|---|
| Partial penectomy | 25 | 61 |
| Total penectomy | 16 | 39 |
Number of missing cases: 0 cases.
Var <- Data$Subtype
categorical.plot(Var, align = "h", left =8)
descriptive.categorical(Var)| No. Cases | % | |
|---|---|---|
| Usual | 24 | 58.5 |
| Verrucous | 7 | 17.1 |
| Papillary | 3 | 7.3 |
| Warty | 3 | 7.3 |
| Mixed | 4 | 9.8 |
Number of missing cases: 0 cases.
Var <- Data$Anatomical
categorical.plot(Var, align = "h", left = 14)
descriptive.categorical(Var)| No. Cases | % | |
|---|---|---|
| Glans alone | 26 | 63 |
| Glans + Coronal sulcus | 6 | 15 |
| Glans + Coronal sulcus + Foreskin | 9 | 22 |
Number of missing cases: 0 cases.
Var <- Data$Level
categorical.plot(Var, align = "h", left = 9)
descriptive.categorical(Var)| No. Cases | % | |
|---|---|---|
| Corpus cavernosum | 27 | 65.9 |
| Corpus spongiosum | 12 | 29.3 |
| Lamina propria | 2 | 4.9 |
Number of missing cases: 0 cases.
Var <- Data$Size
numerical.plot(Var, label = "Tumor Size, cm")
descriptive.numerical(Var)| Statistics | Values |
|---|---|
| Mean | 4.8 |
| Standard Deviation | 2 |
| Median | 5 |
| Interquartile Range | 2.9 |
| Mininum | 1.3 |
| Maximum | 9 |
Number of missing cases: 15 cases.
Var <- Data$Age
numerical.plot(Var, label = "Patient's Age, years")
descriptive.numerical(Var)| Statistics | Values |
|---|---|
| Mean | 56.2 |
| Standard Deviation | 12.3 |
| Median | 55 |
| Interquartile Range | 19 |
| Mininum | 33 |
| Maximum | 85 |
Number of missing cases: 0 cases.
Var <- Data$Lymphadenectomy
categorical.plot(Var)
descriptive.categorical(Var)| No. Cases | % | |
|---|---|---|
| No | 23 | 56 |
| Yes | 18 | 44 |
Number of missing cases: 0 cases.
Var <- Data$Mets
categorical.plot(Var)
descriptive.categorical(Var)| No. Cases | % | |
|---|---|---|
| No | 36 | 88 |
| Yes | 5 | 12 |
Number of missing cases: 0 cases.
Var <- Data$Urethra
categorical.plot(Var)
descriptive.categorical(Var)| No. Cases | % | |
|---|---|---|
| No | 19 | 56 |
| Yes | 15 | 44 |
Number of missing cases: 7 cases.
Var <- Data$Vascular
categorical.plot(Var)
descriptive.categorical(Var)| No. Cases | % | |
|---|---|---|
| No | 36 | 90 |
| Yes | 4 | 10 |
Number of missing cases: 1 cases.
Var <- Data$Perineural
categorical.plot(Var)
descriptive.categorical(Var)| No. Cases | % | |
|---|---|---|
| No | 34 | 85 |
| Yes | 6 | 15 |
Number of missing cases: 1 cases.
Var <- Data$pT
categorical.plot(Var)
descriptive.categorical(Var)| No. Cases | % | |
|---|---|---|
| T1 | 1 | 2.9 |
| T2 | 18 | 52.9 |
| T3 | 15 | 44.1 |
Number of missing cases: 7 cases.
Var <- Data$Relapse
categorical.plot(Var)
descriptive.categorical(Var)| No. Cases | % | |
|---|---|---|
| No | 38 | 95 |
| Yes | 2 | 5 |
Number of missing cases: 1 cases.
Var <- Data$FollowUp
numerical.plot(Var, label = "Follow-Up, Months")
descriptive.numerical(Var)| Statistics | Values |
|---|---|
| Mean | 152.5 |
| Standard Deviation | 101 |
| Median | 157.3 |
| Interquartile Range | 143.6 |
| Mininum | 0.8 |
| Maximum | 386.7 |
Number of missing cases: 0 cases.
Var <- Data$Outcome
categorical.plot(Var)
descriptive.categorical(Var)| No. Cases | % | |
|---|---|---|
| Alive with disease | 3 | 7.3 |
| Alive without disease | 23 | 56.1 |
| Died of cancer | 1 | 2.4 |
| Died of other causes | 14 | 34.1 |
Number of missing cases: 0 cases.
Var <- Data$cN
categorical.plot(Var)
descriptive.categorical(Var)| No. Cases | % | |
|---|---|---|
| cN0 | 20 | 50 |
| cN1 | 7 | 18 |
| cN2 | 13 | 32 |
Number of missing cases: 1 cases.
Var <- Data$Final_Nodal
categorical.plot(Var)
descriptive.categorical(Var)| No. Cases | % | |
|---|---|---|
| Negative | 33 | 80 |
| Positive | 8 | 20 |
Number of missing cases: 0 cases.
Var <- Data$DOD
categorical.plot(Var)
descriptive.categorical(Var)| No. Cases | % | |
|---|---|---|
| No | 40 | 97.6 |
| Yes | 1 | 2.4 |
Number of missing cases: 0 cases.
Var2 <- Data$ParadoxicalVar1 <- Data$Procedure
categorical.group.plot(Var1, Var2)
descriptive.categorical.group(Var1, Var2)| Superficial High-Grade | % | Deep Low-Grade | % | |
|---|---|---|---|---|
| Partial penectomy | 10 | 83.3 | 15 | 51.7 |
| Total penectomy | 2 | 16.7 | 14 | 48.3 |
Var1 <- Data$Anatomical
categorical.group.plot(Var1, Var2)
descriptive.categorical.group(Var1, Var2)| Superficial High-Grade | % | Deep Low-Grade | % | |
|---|---|---|---|---|
| Glans alone | 7 | 58.3 | 19 | 65.5 |
| Glans + Coronal sulcus | 2 | 16.7 | 4 | 13.8 |
| Glans + Coronal sulcus + Foreskin | 3 | 25.0 | 6 | 20.7 |
Var1 <- Data$Level
categorical.group.plot(Var1, Var2)
descriptive.categorical.group(Var1, Var2)| Superficial High-Grade | % | Deep Low-Grade | % | |
|---|---|---|---|---|
| Corpus cavernosum | 0 | 0.0 | 27 | 93.1 |
| Corpus spongiosum | 10 | 83.3 | 2 | 6.9 |
| Lamina propria | 2 | 16.7 | 0 | 0.0 |
Var1 <- Data$Size
numerical.group.plot(Var1, Var2)
descriptive.numerical.group(Var1, Var2)| Superficial High-Grade | Deep Low-Grade | |
|---|---|---|
| Mean | 4.8 | 4.8 |
| Standard Deviation | 3.0 | 1.7 |
| Median | 4.5 | 5.0 |
| Interquartile Range | 4.1 | 2.1 |
| Minimum | 1.3 | 2.0 |
| Maximum | 9.0 | 9.0 |
Var1 <- Data$Age
numerical.group.plot(Var1, Var2)
descriptive.numerical.group(Var1, Var2)| Superficial High-Grade | Deep Low-Grade | |
|---|---|---|
| Mean | 50.8 | 58.4 |
| Standard Deviation | 10.5 | 12.4 |
| Median | 52.5 | 56.0 |
| Interquartile Range | 12.5 | 19.0 |
| Minimum | 33.0 | 37.0 |
| Maximum | 69.0 | 85.0 |
Var1 <- Data$Urethra
categorical.group.plot(Var1, Var2)
descriptive.categorical.group(Var1, Var2)| Superficial High-Grade | % | Deep Low-Grade | % | |
|---|---|---|---|---|
| No | 6 | 66.7 | 13 | 52 |
| Yes | 3 | 33.3 | 12 | 48 |
Var1 <- Data$Vascular
categorical.group.plot(Var1, Var2)
descriptive.categorical.group(Var1, Var2)| Superficial High-Grade | % | Deep Low-Grade | % | |
|---|---|---|---|---|
| No | 10 | 83.3 | 26 | 92.9 |
| Yes | 2 | 16.7 | 2 | 7.1 |
Var1 <- Data$Perineural
categorical.group.plot(Var1, Var2)
descriptive.categorical.group(Var1, Var2)| Superficial High-Grade | % | Deep Low-Grade | % | |
|---|---|---|---|---|
| No | 10 | 83.3 | 24 | 85.7 |
| Yes | 2 | 16.7 | 4 | 14.3 |
Var1 <- Data$cN
categorical.group.plot(Var1, Var2)
descriptive.categorical.group(Var1, Var2)| Superficial High-Grade | % | Deep Low-Grade | % | |
|---|---|---|---|---|
| cN0 | 8 | 66.7 | 12 | 42.9 |
| cN1 | 0 | 0.0 | 7 | 25.0 |
| cN2 | 4 | 33.3 | 9 | 32.1 |
Var1 <- Data$pT
categorical.group.plot(Var1, Var2)
descriptive.categorical.group(Var1, Var2)| Superficial High-Grade | % | Deep Low-Grade | % | |
|---|---|---|---|---|
| T1 | 1 | 11.1 | 0 | 0 |
| T2 | 5 | 55.6 | 13 | 52 |
| T3 | 3 | 33.3 | 12 | 48 |
Var1 <- Data$Mets
categorical.group.plot(Var1, Var2)
descriptive.categorical.group(Var1, Var2)| Superficial High-Grade | % | Deep Low-Grade | % | |
|---|---|---|---|---|
| No | 8 | 66.7 | 28 | 96.6 |
| Yes | 4 | 33.3 | 1 | 3.4 |
Var1 <- Data$Relapse
categorical.group.plot(Var1, Var2)
descriptive.categorical.group(Var1, Var2)| Superficial High-Grade | % | Deep Low-Grade | % | |
|---|---|---|---|---|
| No | 11 | 91.7 | 27 | 96.4 |
| Yes | 1 | 8.3 | 1 | 3.6 |
Var1 <- Data$Final_Nodal
categorical.group.plot(Var1, Var2)
descriptive.categorical.group(Var1, Var2)| Superficial High-Grade | % | Deep Low-Grade | % | |
|---|---|---|---|---|
| Negative | 8 | 66.7 | 25 | 86.2 |
| Positive | 4 | 33.3 | 4 | 13.8 |
Var1 <- Data$DOD
categorical.group.plot(Var1, Var2)
descriptive.categorical.group(Var1, Var2)| Superficial High-Grade | % | Deep Low-Grade | % | |
|---|---|---|---|---|
| No | 12 | 100 | 28 | 96.6 |
| Yes | 0 | 0 | 1 | 3.4 |
source("RiskTable.R")
x.lab <- "Follow-Up, Months"
y.lab <- "Survival Function"
# Defining outcome variable
Status <- Data$Final_Nodal
# Creating dicotomic variables from numerical variables for plotting
Size_Median <- factor(ifelse(Data$Size > median(Data$Size, na.rm = TRUE),
c("Above Median Size"), c("Below Median Size")))
Age_Median <- factor(ifelse(Data$Age > median(Data$Age, na.rm = TRUE),
c("Above Median Age"), c("Below Median Age")))
# By type of tumor
with(Data, survival.plot(Paradoxical, FollowUp, Status,
xlab = x.lab, ylab = y.lab,
title = "Final Nodal Status by Tumor Type"))
with(Data, risk.table(Paradoxical, FollowUp, Status))| 0 | 50 | 100 | 150 | 200 | 250 | 300 | |
|---|---|---|---|---|---|---|---|
| Superficial High-Grade | 12 | 11 | 10 | 9 | 6 | 2 | 2 |
| Deep Low-Grade | 29 | 20 | 16 | 12 | 8 | 4 | 2 |
# By surgical procedure
with(Data, survival.plot(Procedure, FollowUp, Status,
xlab = x.lab, ylab = y.lab,
title = "Final Nodal Status by Surgical Procedure"))
with(Data, risk.table(Procedure, FollowUp, Status))| 0 | 50 | 100 | 150 | 200 | 250 | 300 | |
|---|---|---|---|---|---|---|---|
| Partial penectomy | 25 | 19 | 17 | 14 | 8 | 4 | 3 |
| Total penectomy | 16 | 12 | 9 | 7 | 6 | 2 | 1 |
# By anatomical location
with(Data, survival.plot(Anatomical, FollowUp, Status,
xlab = x.lab, ylab = y.lab, ylim = c(0, 1.09),
title = "Final Nodal Status by Anatomical Location"))
with(Data, risk.table(Anatomical, FollowUp, Status))| 0 | 50 | 100 | 150 | 200 | 250 | 300 | |
|---|---|---|---|---|---|---|---|
| Glans alone | 26 | 19 | 14 | 13 | 10 | 3 | 1 |
| Glans + Coronal sulcus | 6 | 5 | 5 | 3 | 2 | 2 | 2 |
| Glans + Coronal sulcus + Foreskin | 9 | 7 | 7 | 5 | 2 | 1 | 1 |
# By anatomical level
with(Data, survival.plot(Level, FollowUp, Status,
xlab = x.lab, ylab = y.lab, ylim = c(0, 1.09),
title = "Final Nodal Status by Anatomical Level"))
with(Data, risk.table(Level, FollowUp, Status))| 0 | 50 | 100 | 150 | 200 | |
|---|---|---|---|---|---|
| Corpus cavernosum | 27 | 19 | 15 | 12 | 8 |
| Corpus spongiosum | 12 | 10 | 10 | 8 | 5 |
| Lamina propria | 2 | 2 | 1 | 1 | 1 |
# By median tumor size
with(Data, survival.plot(Size_Median, FollowUp, Status,
xlab = x.lab, ylab = y.lab,
title = "Final Nodal Status by Median Tumor Size"))
with(Data, risk.table(Size_Median, FollowUp, Status))| 0 | 50 | 100 | 150 | 200 | |
|---|---|---|---|---|---|
| Above Median Size | 9 | 7 | 6 | 5 | 3 |
| Below Median Size | 17 | 11 | 7 | 4 | 2 |
# By median patient's age
with(Data, survival.plot(Age_Median, FollowUp, Status,
xlab = x.lab, ylab = y.lab,
title = "Final Nodal Status by Median Patient's Age"))
with(Data, risk.table(Age_Median, FollowUp, Status))| 0 | 50 | 100 | 150 | 200 | |
|---|---|---|---|---|---|
| Above Median Age | 19 | 13 | 10 | 7 | 5 |
| Below Median Age | 22 | 18 | 16 | 14 | 9 |
# By urethral invasion
with(Data, survival.plot(Urethra, FollowUp, Status,
xlab = x.lab, ylab = y.lab,
title = "Final Nodal Status by Urethral Invasion"))
with(Data, risk.table(Urethra, FollowUp, Status))| 0 | 50 | 100 | 150 | 200 | 250 | 300 | |
|---|---|---|---|---|---|---|---|
| No | 19 | 14 | 12 | 10 | 6 | 2 | 1 |
| Yes | 15 | 11 | 9 | 7 | 4 | 3 | 3 |
# By vascular invasion
with(Data, survival.plot(Vascular, FollowUp, Status,
xlab = x.lab, ylab = y.lab,
title = "Final Nodal Status by Vascular Invasion"))
with(Data, risk.table(Vascular, FollowUp, Status))| 0 | 50 | 100 | 150 | 200 | |
|---|---|---|---|---|---|
| No | 36 | 27 | 22 | 17 | 12 |
| Yes | 4 | 3 | 3 | 3 | 2 |
# By perineural invasion
with(Data, survival.plot(Perineural, FollowUp, Status,
xlab = x.lab, ylab = y.lab,
title = "Final Nodal Status by Perineural Invasion"))
with(Data, risk.table(Perineural, FollowUp, Status))| 0 | 50 | 100 | 150 | 200 | 250 | |
|---|---|---|---|---|---|---|
| No | 34 | 26 | 21 | 16 | 11 | 5 |
| Yes | 6 | 4 | 4 | 4 | 3 | 1 |
# By pathological T stage
with(Data, survival.plot(pT, FollowUp, Status,
xlab = x.lab, ylab = y.lab,
title = "Final Nodal Status by Pathological T Stage"))
with(Data, risk.table(pT, FollowUp, Status))| 0 | 50 | |
|---|---|---|
| T1 | 1 | 1 |
| T2 | 18 | 13 |
| T3 | 15 | 11 |
# By clinical N stage
with(Data, survival.plot(cN, FollowUp, Status,
xlab = x.lab, ylab = y.lab,
title = "Final Nodal Status by Clinical N Stage"))
with(Data, risk.table(cN, FollowUp, Status))| 0 | 50 | 100 | |
|---|---|---|---|
| cN0 | 20 | 16 | 13 |
| cN1 | 7 | 3 | 2 |
| cN2 | 13 | 11 | 10 |
# Defining outcome variable
Status <- Data$DOD
# Creating dicotomic variables from numerical variables for plotting
Size_Median <- factor(ifelse(Data$Size > median(Data$Size, na.rm = TRUE),
c("Above Median Size"), c("Below Median Size")))
Age_Median <- factor(ifelse(Data$Age > median(Data$Age, na.rm = TRUE),
c("Above Median Age"), c("Below Median Age")))
# By type of tumor
with(Data, survival.plot(Paradoxical, FollowUp, Status,
ylim = c(0.6, 1), position = "bottomright", xlab = x.lab, ylab = y.lab,
title = "Cancer-Related Death by Tumor Type"))
with(Data, risk.table(Paradoxical, FollowUp, Status))| 0 | 50 | 100 | 150 | 200 | 250 | 300 | |
|---|---|---|---|---|---|---|---|
| Superficial High-Grade | 12 | 11 | 10 | 9 | 6 | 2 | 2 |
| Deep Low-Grade | 29 | 20 | 16 | 12 | 8 | 4 | 2 |
# By surgical procedure
with(Data, survival.plot(Procedure, FollowUp, Status,
ylim = c(0.6, 1), position = "bottomright", xlab = x.lab, ylab = y.lab,
title = "Cancer-Related Death by Surgical Procedure"))
with(Data, risk.table(Procedure, FollowUp, Status))| 0 | 50 | 100 | 150 | 200 | 250 | 300 | |
|---|---|---|---|---|---|---|---|
| Partial penectomy | 25 | 19 | 17 | 14 | 8 | 4 | 3 |
| Total penectomy | 16 | 12 | 9 | 7 | 6 | 2 | 1 |
# By anatomical location
with(Data, survival.plot(Anatomical, FollowUp, Status,
ylim = c(0.6, 1), position = "bottomright", xlab = x.lab, ylab = y.lab,
title = "Cancer-Related Death by Anatomical Location"))
with(Data, risk.table(Anatomical, FollowUp, Status))| 0 | 50 | 100 | 150 | 200 | 250 | 300 | |
|---|---|---|---|---|---|---|---|
| Glans alone | 26 | 19 | 14 | 13 | 10 | 3 | 1 |
| Glans + Coronal sulcus | 6 | 5 | 5 | 3 | 2 | 2 | 2 |
| Glans + Coronal sulcus + Foreskin | 9 | 7 | 7 | 5 | 2 | 1 | 1 |
# By anatomical level
with(Data, survival.plot(Level, FollowUp, Status,
ylim = c(0.6, 1), position = "bottomright", xlab = x.lab, ylab = y.lab,
title = "Cancer-Related Death by Anatomical Level"))
with(Data, risk.table(Level, FollowUp, Status))| 0 | 50 | 100 | 150 | 200 | |
|---|---|---|---|---|---|
| Corpus cavernosum | 27 | 19 | 15 | 12 | 8 |
| Corpus spongiosum | 12 | 10 | 10 | 8 | 5 |
| Lamina propria | 2 | 2 | 1 | 1 | 1 |
# By median tumor size
with(Data, survival.plot(Size_Median, FollowUp, Status,
ylim = c(0.6, 1), position = "bottomright", xlab = x.lab, ylab = y.lab,
title = "Cancer-Related Death by Median Tumor Size"))
with(Data, risk.table(Size_Median, FollowUp, Status))| 0 | 50 | 100 | 150 | 200 | |
|---|---|---|---|---|---|
| Above Median Size | 9 | 7 | 6 | 5 | 3 |
| Below Median Size | 17 | 11 | 7 | 4 | 2 |
# By median patient's age
with(Data, survival.plot(Age_Median, FollowUp, Status,
ylim = c(0.6, 1), position = "bottomright", xlab = x.lab, ylab = y.lab,
title = "Cancer-Related Death by Median Patient's Age"))
with(Data, risk.table(Age_Median, FollowUp, Status))| 0 | 50 | 100 | 150 | 200 | |
|---|---|---|---|---|---|
| Above Median Age | 19 | 13 | 10 | 7 | 5 |
| Below Median Age | 22 | 18 | 16 | 14 | 9 |
# By urethral invasion
with(Data, survival.plot(Urethra, FollowUp, Status,
ylim = c(0.6, 1), position = "bottomright", xlab = x.lab, ylab = y.lab,
title = "Cancer-Related Death by Urethral Invasion"))
with(Data, risk.table(Urethra, FollowUp, Status))| 0 | 50 | 100 | 150 | 200 | 250 | 300 | |
|---|---|---|---|---|---|---|---|
| No | 19 | 14 | 12 | 10 | 6 | 2 | 1 |
| Yes | 15 | 11 | 9 | 7 | 4 | 3 | 3 |
# By vascular invasion
with(Data, survival.plot(Vascular, FollowUp, Status,
ylim = c(0.6, 1), position = "bottomright", xlab = x.lab, ylab = y.lab,
title = "Cancer-Related Death by Vascular Invasion"))
with(Data, risk.table(Vascular, FollowUp, Status))| 0 | 50 | 100 | 150 | 200 | |
|---|---|---|---|---|---|
| No | 36 | 27 | 22 | 17 | 12 |
| Yes | 4 | 3 | 3 | 3 | 2 |
# By perineural invasion
with(Data, survival.plot(Perineural, FollowUp, Status,
ylim = c(0.6, 1), position = "bottomright", xlab = x.lab, ylab = y.lab,
title = "Cancer-Related Death by Perineural Invasion"))
with(Data, risk.table(Perineural, FollowUp, Status))| 0 | 50 | 100 | 150 | 200 | 250 | |
|---|---|---|---|---|---|---|
| No | 34 | 26 | 21 | 16 | 11 | 5 |
| Yes | 6 | 4 | 4 | 4 | 3 | 1 |
# By pathological T stage
with(Data, survival.plot(pT, FollowUp, Status,
ylim = c(0.6, 1), position = "bottomright", xlab = x.lab, ylab = y.lab,
title = "Cancer-Related Death by Pathological T Stage"))
with(Data, risk.table(pT, FollowUp, Status))| 0 | 50 | |
|---|---|---|
| T1 | 1 | 1 |
| T2 | 18 | 13 |
| T3 | 15 | 11 |
# By clinical N stage
with(Data, survival.plot(cN, FollowUp, Status,
ylim = c(0.6, 1), position = "bottomright", xlab = x.lab, ylab = y.lab,
title = "Cancer-Related Death by Clinical N Stage"))
with(Data, risk.table(cN, FollowUp, Status))| 0 | 50 | 100 | |
|---|---|---|---|
| cN0 | 20 | 16 | 13 |
| cN1 | 7 | 3 | 2 |
| cN2 | 13 | 11 | 10 |
# Creating variables for GLM analysis
Paradoxical_Inv <- factor(Data$Paradoxical,
levels = c("Deep Low-Grade", "Superficial High-Grade"))
Data$Multicompartment <- factor(ifelse(Data$Anatomical == "Glans alone",
c("No"), c("Yes")))
Data$CC <- factor(ifelse(Data$Level == "Corpus cavernosum",
c("Yes"), c("No")))
Data$High_pT <- factor(ifelse(Data$pT == "T3",
c("Yes"), c("No")))
Data$cN_Positive <- factor(ifelse(Data$cN == "cN0",
c("No"), c("Yes")))
# Creating list of variables and labels
Predictors <- with(Data, list(
"Superficial high-grade vs. deep low-grade" = Paradoxical_Inv,
"Primary treatment" = Procedure,
"Age > median" = Age_Median,
"Multicompartment tumor" = Multicompartment,
"Invasion of corpus cavernosum" = CC,
"Tumor size > median" = Size_Median,
"Invasion of penile urethra" = Urethra,
"Vascular invasion" = Vascular,
"Perineural invasion" = Perineural,
"High pT (>pT3)" = High_pT,
"Positive clinical nodes" = cN_Positive
))
Varlabels <- names(Predictors)Outcome <- Data$Mets
logistic.table(Outcome, Predictors, Varlabels)| Variables | OR | Lower 95% CI | Higher 95% CI | P value |
|---|---|---|---|---|
| Superficial high-grade vs. deep low-grade | 14.00 | 1.77 | 295.70 | 0.026 |
| Primary treatment | 1.3e-08 | NA | 2.1e+117 | 0.99 |
| Age > median | 0.53 | 0.064 | 3.59 | 0.52 |
| Multicompartment tumor | 1.18 | 0.14 | 8.03 | 0.87 |
| Invasion of corpus cavernosum | 0.096 | 0.0046 | 0.75 | 0.047 |
| Tumor size > median | 1.07 | 0.088 | 25.05 | 0.96 |
| Invasion of penile urethra | 0.27 | 0.013 | 2.09 | 0.26 |
| Vascular invasion | 11.00 | 1.03 | 127.12 | 0.04 |
| Perineural invasion | 5.17 | 0.56 | 42.50 | 0.12 |
| High pT (>pT3) | 0.27 | 0.013 | 2.09 | 0.26 |
| Positive clinical nodes | 1.59 | 0.24 | 13.20 | 0.63 |
Outcome <- Data$Relapse
logistic.table(Outcome, Predictors, Varlabels)| Variables | OR | Lower 95% CI | Higher 95% CI | P value |
|---|---|---|---|---|
| Superficial high-grade vs. deep low-grade | 2.45 | 0.092 | 65.89 | 0.54 |
| Primary treatment | 1.71 | 0.064 | 45.68 | 0.71 |
| Age > median | 0.81 | 0.03 | 21.49 | 0.88 |
| Multicompartment tumor | 1.4e-08 | NA | Inf | 1 |
| Invasion of corpus cavernosum | 0.52 | 0.019 | 13.87 | 0.65 |
| Tumor size > median | 0.50 | 0.018 | 13.76 | 0.64 |
| Invasion of penile urethra | 1.21 | 0.045 | 32.60 | 0.89 |
| Vascular invasion | 1.4e-07 | NA | 2.4e+227 | 1 |
| Perineural invasion | 6.40 | 0.23 | 180.98 | 0.21 |
| High pT (>pT3) | 1.21 | 0.045 | 32.60 | 0.89 |
| Positive clinical nodes | 9.5e+07 | 1.8e-282 | NA | 1 |
Outcome <- Data$Final_Nodal
logistic.table(Outcome, Predictors, Varlabels)| Variables | OR | Lower 95% CI | Higher 95% CI | P value |
|---|---|---|---|---|
| Superficial high-grade vs. deep low-grade | 3.12 | 0.61 | 16.27 | 0.16 |
| Primary treatment | 0.45 | 0.06 | 2.31 | 0.37 |
| Age > median | 0.44 | 0.08 | 2.11 | 0.31 |
| Multicompartment tumor | 1.05 | 0.19 | 5.08 | 0.95 |
| Invasion of corpus cavernosum | 0.43 | 0.086 | 2.17 | 0.3 |
| Tumor size > median | 0.75 | 0.10 | 6.70 | 0.78 |
| Invasion of penile urethra | 0.70 | 0.12 | 3.48 | 0.67 |
| Vascular invasion | 18.60 | 1.96 | 421.69 | 0.019 |
| Perineural invasion | 5.80 | 0.87 | 40.60 | 0.064 |
| High pT (>pT3) | 0.70 | 0.12 | 3.48 | 0.67 |
| Positive clinical nodes | 1.89 | 0.40 | 10.50 | 0.43 |
Tumor median size was removed due to the lack of events in either level.
Outcome <- Data$DOD
Predictors_DOD <- Predictors[-grep("Tumor size > median", Varlabels)]
Varlabels_DOD <- names(Predictors_DOD)
logistic.table(Outcome, Predictors_DOD, Varlabels_DOD)| Variables | OR | Lower 95% CI | Higher 95% CI | P value |
|---|---|---|---|---|
| Superficial high-grade vs. deep low-grade | 3.3e-08 | NA | Inf | 1 |
| Primary treatment | 1.5e+08 | 0.00 | NA | 1 |
| Age > median | 7.7e-09 | NA | Inf | 1 |
| Multicompartment tumor | 1.7e+08 | 0.00 | NA | 1 |
| Invasion of corpus cavernosum | 3.3e+07 | 0.00 | NA | 1 |
| Invasion of penile urethra | 2.1e-08 | NA | Inf | 1 |
| Vascular invasion | 1.1e-07 | NA | Inf | 1 |
| Perineural invasion | 1e-07 | NA | Inf | 1 |
| High pT (>pT3) | 2.1e-08 | NA | Inf | 1 |
| Positive clinical nodes | 8.2e-09 | NA | Inf | 1 |
Outcome <- Data$Mets
FU <- Data$FollowUp
cox.table(Outcome, FU, Predictors, Varlabels)| Variables | HR | Lower 95% CI | Higher 95% CI | P value |
|---|---|---|---|---|
| Superficial high-grade vs. deep low-grade | 6.13 | 0.68 | 55.02 | 0.065 |
| Primary treatment | 2.7e-09 | 0.00 | Inf | 0.036 |
| Age > median | 0.37 | 0.061 | 2.21 | 0.27 |
| Multicompartment tumor | 1.26 | 0.21 | 7.65 | 0.8 |
| Invasion of corpus cavernosum | 0.18 | 0.02 | 1.60 | 0.079 |
| Tumor size > median | 1.68 | 0.15 | 18.85 | 0.67 |
| Invasion of penile urethra | 0.37 | 0.042 | 3.34 | 0.34 |
| Vascular invasion | 4.57 | 0.75 | 27.71 | 0.13 |
| Perineural invasion | 2.92 | 0.48 | 17.68 | 0.27 |
| High pT (>pT3) | 0.37 | 0.042 | 3.34 | 0.34 |
| Positive clinical nodes | 2.26 | 0.37 | 13.82 | 0.37 |
Outcome <- Data$Relapse
cox.table(Outcome, FU, Predictors, Varlabels)| Variables | HR | Lower 95% CI | Higher 95% CI | P value |
|---|---|---|---|---|
| Superficial high-grade vs. deep low-grade | 2.04 | 0.13 | 32.62 | 0.62 |
| Primary treatment | 1.61 | 0.10 | 25.78 | 0.74 |
| Age > median | 0.74 | 0.046 | 11.81 | 0.83 |
| Multicompartment tumor | 2.6e-09 | 0.00 | Inf | 0.17 |
| Invasion of corpus cavernosum | 0.62 | 0.039 | 9.99 | 0.74 |
| Tumor size > median | 0.57 | 0.035 | 9.06 | 0.69 |
| Invasion of penile urethra | 1.37 | 0.085 | 22.01 | 0.82 |
| Vascular invasion | 1.3e-08 | 0.00 | Inf | 0.55 |
| Perineural invasion | 6.09 | 0.38 | 97.49 | 0.23 |
| High pT (>pT3) | 1.37 | 0.085 | 22.01 | 0.82 |
| Positive clinical nodes | 1.7e+09 | 0.00 | Inf | 0.092 |
Outcome <- Data$Final_Nodal
cox.table(Outcome, FU, Predictors, Varlabels)| Variables | HR | Lower 95% CI | Higher 95% CI | P value |
|---|---|---|---|---|
| Superficial high-grade vs. deep low-grade | 1.77 | 0.44 | 7.14 | 0.43 |
| Primary treatment | 0.57 | 0.12 | 2.84 | 0.48 |
| Age > median | 0.38 | 0.089 | 1.59 | 0.17 |
| Multicompartment tumor | 1.10 | 0.26 | 4.63 | 0.9 |
| Invasion of corpus cavernosum | 0.65 | 0.16 | 2.60 | 0.54 |
| Tumor size > median | 1.06 | 0.17 | 6.47 | 0.95 |
| Invasion of penile urethra | 0.83 | 0.20 | 3.46 | 0.79 |
| Vascular invasion | 4.63 | 1.09 | 19.62 | 0.058 |
| Perineural invasion | 2.91 | 0.69 | 12.28 | 0.17 |
| High pT (>pT3) | 0.83 | 0.20 | 3.46 | 0.79 |
| Positive clinical nodes | 2.12 | 0.50 | 9.06 | 0.3 |
Outcome <- Data$DOD
cox.table(Outcome, FU, Predictors, Varlabels)| Variables | HR | Lower 95% CI | Higher 95% CI | P value |
|---|---|---|---|---|
| Superficial high-grade vs. deep low-grade | 3.2e-09 | 0.00 | Inf | 0.41 |
| Primary treatment | 1e+09 | 0.00 | Inf | 0.17 |
| Age > median | 1.4e-09 | 0.00 | Inf | 0.21 |
| Multicompartment tumor | 1.2e+09 | 0.00 | Inf | 0.16 |
| Invasion of corpus cavernosum | 3.6e+08 | 0.00 | Inf | 0.36 |
| Tumor size > median | 1.00 | 1.00 | 1.00 | 1 |
| Invasion of penile urethra | 2.1e-09 | 0.00 | Inf | 0.28 |
| Vascular invasion | 1.3e-08 | 0.00 | Inf | 0.65 |
| Perineural invasion | 4.3e-09 | 0.00 | Inf | 0.57 |
| High pT (>pT3) | 2.1e-09 | 0.00 | Inf | 0.28 |
| Positive clinical nodes | 1.7e-09 | 0.00 | Inf | 0.24 |