An Excel program for calculating and plotting receiver-operator characteristic (ROC) curves, histograms and descriptive statistics.

G Görög

doi:10.1016/0010-4825(94)90012-4

Home/Archive/PubMed

PubMed · 7924261

An Excel program for calculating and plotting receiver-operator characteristic (ROC) curves, histograms and descriptive statistics.

G Görög

View original source ↗Export metadata ↓

Abstract

Receiver-operator characteristic (ROC) curves have been used increasingly to assess the performance of clinical laboratory tests and to determine suitable positive/negative threshold values. The Microsoft Excel 4.0 program Plot.ROC was designed to provide formatted, publication quality output with minimal user involvement. It asks for a few parameters at the beginning and then runs autonomously, creating ROC charts, histograms and descriptive statistics for each test chosen, and a cumulative ROC chart to compare tests. Output can easily be formatted further using Excel's graphics and text formatting capabilities.

Explore related subjects

Biomarkers, Tumor Data Interpretation, Statistical Humans Mathematical Computing ROC Curve Software Journal Article

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

G Görög. 1994. An Excel program for calculating and plotting receiver-operator characteristic (ROC) curves, histograms and descriptive statistics.. https://doi.org/10.1016/0010-4825(94)90012-4

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2022

High tumor amplification burden is associated with TP53 mutations in the pan-cancer setting.

Next-generation sequencing data is fundamentally changing the clinical management of patients with cancer. The most frequent genomic alterations in malignancy are mutations and amplifications, with a subset of tumors having multiple amplifications - "amplificators". We sought to understand the molecular correlates of high tumor amplification burden in a pan-cancer context. Using both national registries and a single-institution dataset, our results demonstrate that cancers with TP53 mutations (as compared to those with wild-type TP53) exhibited significantly higher tumor amplification burden across all datasets. Amplifications, generally associated with overexpression, may be potentially actionable secondary consequences of TP53 mutations.

Biomarkers, Tumor↗

PubMed2021

Immune biomarkers and response to checkpoint inhibition of BRAFV600 and BRAF non-V600 altered lung cancers.

BACKGROUND: While 2-4% of lung cancers possess alterations in BRAF, little is known about the immune responsiveness of these tumours. METHODS: Clinical and genomic data were collected from 5945 patients with lung cancers whose tumours underwent next-generation sequencing between 2015 and 2018. Patients were followed through 2020. RESULTS: In total, 127 patients with metastatic BRAF-altered lung cancers were identified: 29 tumours had Class I mutations, 59 had Class II/III alterations, and 39 had variants of unknown significance (VUS). Tumour mutation burden was higher in Class II/III than Class I-altered tumours (8.8 mutations/Mb versus 4.9, P < 0.001), but this difference was diminished when stratified by smoking status. The overall response rate to immune checkpoint inhibitors (ICI) was 9% in Class I-altered tumours and 26% in Class II/III (P = 0.25), with median time on treatment of 1.9 months in both groups. Among patients with Class I-III-altered tumours, 36-month HR for death in those who ever versus never received ICI was 1.82 (1.17-6.11). Nine patients were on ICI for >2 years (two with Class I mutations, two with Class II/III alterations, and five with VUS). CONCLUSIONS: A subset of patients with BRAF-altered lung cancers achieved durable disease control on ICI. However, collectively no significant clinical benefit was seen.

Biomarkers, Tumor↗

PubMed2007

Detection of S100 protein from prostatic cancer patients using anti-S100 protein antibody immobilized on POS-PVA discs.

The S100 proteins have been extensively used as cancer biomarkers. The objectives of the present work were to immobilize the antibody anti-protein S100 to a net of semi-interpenetrated of polysiloxane and polyvinyl alcohol (POS-PVA discs), to investigate its capacity to capture S100 protein from serum and to quantify it by ELISA in sera from patients with prostatic adenocarcinoma (n = 15) and healthy individuals (n = 10). Also these values were compared to the S100 protein expression in the prostatic tissue through immunohistochemistry. The POS-PVA discs fixed about 92.8% of the offered antibody (7.75 microg of antibody per disc). The best values of the immobilized no-marked antibody anti-S100 and serum dilution were found to be 10 microg and 1:400, respectively. Optical density (OD) values for the sera of patients (0.425 +/- 0.042) with prostatic adenocarcinoma were significantly lower (P < 0.05) compared to those established for the healthy individuals (1.034 +/- 0.124). In the immunohistochemistry study no significant variations were observed in the number of positive S100 cells between prostatic adenocarcinoma (153.45 +/- 16.82) and normal prostate (147.04 +/- 18.98). These results showed a clear difference between S100 proteins expressed in tissue and presented in serum during the prostatic tissue neoplasic transformation. Sera analysis was more sensitive than immunohistochemistry S100 protein detection in the prostate tissue besides the advantage to be less invasive method.

Biomarkers, Tumor↗