This dataset contains Cancer Incidence data for Prostate Cancer(All Stages^) including: Age-Adjusted Rate, Confidence Interval, Average Annual Count, and Trend field information for US States for the average 5 year span from 2016 to 2020.Data are for males segmented age (All Ages, Ages Under 50, Ages 50 & Over, Ages Under 65, and Ages 65 & Over), with field names and aliases describing the sex and age group tabulated.For more information, visit statecancerprofiles.cancer.govData NotationsState Cancer Registries may provide more current or more local data.TrendRising when 95% confidence interval of average annual percent change is above 0.Stable when 95% confidence interval of average annual percent change includes 0.Falling when 95% confidence interval of average annual percent change is below 0.† Incidence rates (cases per 100,000 population per year) are age-adjusted to the 2000 US standard population (19 age groups: <1, 1-4, 5-9, ... , 80-84, 85+). Rates are for invasive cancer only (except for bladder cancer which is invasive and in situ) or unless otherwise specified. Rates calculated using SEER*Stat. Population counts for denominators are based on Census populations as modified by NCI. The US Population Data File is used for SEER and NPCR incidence rates.‡ Incidence Trend data come from different sources. Due to different years of data availability, most of the trends are AAPCs based on APCs but some are APCs calculated in SEER*Stat. Please refer to the source for each area for additional information.Rates and trends are computed using different standards for malignancy. For more information see malignant.^ All Stages refers to any stage in the Surveillance, Epidemiology, and End Results (SEER) summary stage.Data Source Field Key(1) Source: National Program of Cancer Registries and Surveillance, Epidemiology, and End Results SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute. Based on the 2022 submission.(5) Source: National Program of Cancer Registries and Surveillance, Epidemiology, and End Results SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute. Based on the 2022 submission.(6) Source: National Program of Cancer Registries SEER*Stat Database - United States Department of Health and Human Services, Centers for Disease Control and Prevention (based on the 2022 submission).(7) Source: SEER November 2022 submission.(8) Source: Incidence data provided by the SEER Program. AAPCs are calculated by the Joinpoint Regression Program and are based on APCs. Data are age-adjusted to the 2000 US standard population (19 age groups: <1, 1-4, 5-9, ... , 80-84,85+). Rates are for invasive cancer only (except for bladder cancer which is invasive and in situ) or unless otherwise specified. Population counts for denominators are based on Census populations as modified by NCI. The US Population Data File is used with SEER November 2022 data.Some data are not available, see Data Not Available for combinations of geography, cancer site, age, and race/ethnicity.Data for the United States does not include data from Nevada.Data for the United States does not include Puerto Rico.

ObjectiveUsing the latest cohort study of prostate cancer patients, explore the epidemiological trend and prognostic factors, and develop a new nomogram to predict the specific survival rate of prostate cancer patients.MethodsPatients with prostate cancer diagnosed from January 1, 1975 to December 31, 2019 in the Surveillance, Epidemiology, and End Results Program (SEER) database were extracted by SEER stat software for epidemiological trend analysis. General clinical information and follow-up data were also collected from 105 135 patients with pathologically diagnosed prostate cancer from January 1, 2010 to December 1, 2019. The factors affecting patient-specific survival were analyzed by Cox regression, and the factors with the greatest influence on specific survival were selected by stepwise regression method, and nomogram was constructed. The model was evaluated by calibration plots, ROC curves, Decision Curve Analysis and C-index.ResultsThere was no significant change in the age-adjusted incidence of prostate cancer from 1975 to 2019, with an average annual percentage change (AAPC) of 0.45 (95% CI:-0.87~1.80). Among the tumor grade, the most significant increase in the incidence of G2 prostate cancer was observed, with an AAPC of 2.99 (95% CI:1.47~4.54); the most significant decrease in the incidence of G4 prostate cancer was observed, with an AAPC of -10.39 (95% CI:-13.86~-6.77). Among the different tumor stages, the most significant reduction in the incidence of localized prostate cancer was observed with an AAPC of -1.83 (95% CI:-2.76~-0.90). Among different races, the incidence of prostate cancer was significantly reduced in American Indian or Alaska Native and Asian or Pacific Islander, with an AAPC of -3.40 (95% CI:-3.97~-2.82) and -2.74 (95% CI:-4.14~-1.32), respectively. Among the different age groups, the incidence rate was significantly increased in 15-54 and 55-64 age groups with AAPC of 4.03 (95% CI:2.73~5.34) and 2.50 (95% CI:0.96~4.05), respectively, and significantly decreased in ≥85 age group with AAPC of -2.50 (95% CI:-3.43~-1.57). In addition, age, tumor stage, race, PSA and gleason score were found to be independent risk factors affecting prostate cancer patient-specific survival. Age, tumor stage, PSA and gleason score were most strongly associated with prostate cancer patient-specific survival by stepwise regression screening, and nomogram prediction model was constructed using these factors. The Concordance indexes are 0.845 (95% CI:0.818~0.872) and 0.835 (95% CI:0.798~0.872) for the training and validation sets, respectively, and the area under the ROC curves (AUC) at 3, 6, and 9 years was 0.7 or more for both the training and validation set samples. The calibration plots indicated a good agreement between the predicted and actual values of the model.ConclusionsAlthough there was no significant change in the overall incidence of prostate cancer in this study, significant changes occurred in the incidence of prostate cancer with different characteristics. In addition, the nomogram prediction model of prostate cancer-specific survival rate constructed based on four factors has a high reference value, which helps physicians to correctly assess the patient-specific survival rate and provides a reference basis for patient diagnosis and prognosis evaluation.

Population based cancer incidence rates were abstracted from National Cancer Institute, State Cancer Profiles for all available counties in the United States for which data were available. This is a national county-level database of cancer data that are collected by state public health surveillance systems. All-site cancer is defined as any type of cancer that is captured in the state registry data, though non-melanoma skin cancer is not included. All-site age-adjusted cancer incidence rates were abstracted separately for males and females. County-level annual age-adjusted all-site cancer incidence rates for years 2006–2010 were available for 2687 of 3142 (85.5%) counties in the U.S. Counties for which there are fewer than 16 reported cases in a specific area-sex-race category are suppressed to ensure confidentiality and stability of rate estimates; this accounted for 14 counties in our study. Two states, Kansas and Virginia, do not provide data because of state legislation and regulations which prohibit the release of county level data to outside entities. Data from Michigan does not include cases diagnosed in other states because data exchange agreements prohibit the release of data to third parties. Finally, state data is not available for three states, Minnesota, Ohio, and Washington. The age-adjusted average annual incidence rate for all counties was 453.7 per 100,000 persons. We selected 2006–2010 as it is subsequent in time to the EQI exposure data which was constructed to represent the years 2000–2005. We also gathered data for the three leading causes of cancer for males (lung, prostate, and colorectal) and females (lung, breast, and colorectal). The EQI was used as an exposure metric as an indicator of cumulative environmental exposures at the county-level representing the period 2000 to 2005. A complete description of the datasets used in the EQI are provided in Lobdell et al. and methods used for index construction are described by Messer et al. The EQI was developed for the period 2000– 2005 because it was the time period for which the most recent data were available when index construction was initiated. The EQI includes variables representing each of the environmental domains. The air domain includes 87 variables representing criteria and hazardous air pollutants. The water domain includes 80 variables representing overall water quality, general water contamination, recreational water quality, drinking water quality, atmospheric deposition, drought, and chemical contamination. The land domain includes 26 variables representing agriculture, pesticides, contaminants, facilities, and radon. The built domain includes 14 variables representing roads, highway/road safety, public transit behavior, business environment, and subsidized housing environment. The sociodemographic environment includes 12 variables representing socioeconomics and crime. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jagai, J., L. Messer, K. Rappazzo , C. Gray, S. Grabich , and D. Lobdell. County-level environmental quality and associations with cancer incidence#. Cancer. John Wiley & Sons Incorporated, New York, NY, USA, 123(15): 2901-2908, (2017).

IntroductionSociodemographic disparities in genitourinary cancer-related mortality have been insufficiently studied, particularly across multiple cancer types. This study aimed to investigate gender, racial, and geographic disparities in mortality rates for the most common genitourinary cancers in the United States.MethodsMortality data for prostate, bladder, kidney, and testicular cancers were obtained from the Centers for Disease Control and Prevention (CDC) WONDER database between 1999 and 2020. Age-adjusted mortality rates (AAMRs) were analyzed by year, gender, race, urban–rural status, and geographic region using a significance level of p < 0.05.ResultsOverall, AAMRs for prostate, bladder, and kidney cancer declined significantly, while testicular cancer-related mortality remained stable. Bladder and kidney cancer AAMRs were 3–4 times higher in males than females. Prostate cancer mortality was highest in black individuals/African Americans and began increasing after 2015. Bladder cancer mortality decreased significantly in White individuals, Black individuals, African Americans, and Asians/Pacific Islanders but remained stable in American Indian/Alaska Natives. Kidney cancer-related mortality was highest in White individuals but declined significantly in other races. Testicular cancer mortality increased significantly in White individuals but remained stable in Black individuals and African Americans. Genitourinary cancer mortality decreased in metropolitan areas but either increased (bladder and testicular cancer) or remained stable (kidney cancer) in non-metropolitan areas. Prostate and kidney cancer mortality was highest in the Midwest, bladder cancer in the South, and testicular cancer in the West.DiscussionSignificant sociodemographic disparities exist in the mortality trends of genitourinary cancers in the United States. These findings highlight the need for targeted interventions and further research to address these disparities and improve outcomes for all populations affected by genitourinary cancers.

This dataset was derived from tracked biopsy sessions using the Artemis biopsy system, many of which included image fusion with MRI targets. Patients received a 3D transrectal ultrasound scan, after which nonrigid registration (e.g. “fusion”) was performed between real-time ultrasound and preoperative MRI, enabling biopsy cores to be sampled from MR regions of interest. Most cases also included sampling of systematic biopsy cores using a 12-core digital template. The Artemis system tracked targeted and systematic core locations using encoder kinematics of a mechanical arm, and recorded locations relative to the Ultrasound scan. MRI biopsy coordinates were also recorded for most cases. STL files and biopsy overlays are available and can be visualized in 3D Slicer with the SlicerHeart extension. Spreadsheets summarizing biopsy and MR target data are also available. See the Detailed Description tab below for more information.

MRI targets were defined using multiparametric MRI, e.g. t2-weighted, diffusion-weighted, and perfusion-weighted sequences, and scored on a Likert-like scale with close correspondence to PIRADS version 2. t2-weighted MRI was used to trace ROI contours, and is the only sequence provided in this dataset. MR imaging was performed on a 3 Tesla Trio, Verio or Skyra scanner (Siemens, Erlangen, Germany). A transabdominal phased array was used in all cases, and an endorectal coil was used in a subset of cases. The majority of pulse sequences are 3D T2:SPC, with TR/TE 2200/203, Matrix/FOV 256 × 205/14 × 14 cm, and 1.5mm slice spacing. Some cases were instead 3D T2:TSE with TR/TE 3800–5040/101, and a small minority were imported from other institutions (various T2 protocols.)

Ultrasound scans were performed with Hitachi Hi-Vision 5500 7.5 MHz or the Noblus C41V 2-10 MHz end-fire probe. 3D scans were acquired by rotation of the end-fire probe 200 degrees about its axis, and interpolating to resample the volume with isotropic resolution.

Patients with suspicion of prostate cancer due to elevated PSA and/or suspicious imaging findings were consecutively accrued. Any consented patient who underwent or had planned to receive a routine, standard-of-care prostate biopsy at the UCLA Clark Urology Center was included.

Note: Some Private Tags in this collection are critical to properly displaying the STL surface and the Prostate anatomy. Private Tag (1129,"Eigen, Inc",1016) DS VoxelSize is especially important for multi-frame US cases.

Note: Our 245 TCGA cases are ones we identified as having potential for improvement. We plan to upload them in two phases: the first batch of 138 cases, and the second batch of 107 cases in the quality review pipeline, we plan to upload them around early of January, 2025.

  Dataset: A Second Opinion on TCGA PRAD Prostate Dataset Labels with ROI-Level Annotations





  Overview

This dataset provides enhanced Gleason grading annotations for the TCGA PRAD prostate cancer dataset… See the full description on the dataset page: https://huggingface.co/datasets/Codatta/Refined-TCGA-PRAD-Prostate-Cancer-Pathology-Dataset.

Introduction

The Surgical Planning Laboratory (SPL) and the National Center for Image Guided Therapy (NCIGT) are making this dataset available as a resource to aid in the development of algorithms and tools for deformable registration, segmentation and analysis of prostate magnetic resonance imaging (MRI) and ultrasound (US) images.

Description

This dataset contains anonymized images of the human prostate (N=3 patients) collected during two sessions for each patient:

1. MRI examination of the prostate for the purposes of disease staging.
2. US examination of the prostate for the purposes of volumetric examination in preparation to the brachytherapy implant.

These are three-dimensional (multi-slice) scalar images.

Image files are stored using NRRD file format (files with .nrrd extension), see details at http://teem.sourceforge.net/nrrd/format.html. Each image file includes a code for the case number (internal numbering at the research site) and the modality (US or MR).

Image annotations were prepared by Dr. Fedorov (no professional training in radiology) and Dr. Tuncali (10+ in prostate imaging interpretation). Annotations include

1. Manual contouring (segmentation) of the whole prostate gland, performed in 3D Slicer software. These segmentation images are coded in the same fashion as the image files, and saved in NRRD format, with "-label" suffix.
2. Manually placed points (fiducials) corresponding to the location of urethra entry into the prostate at base (coded as UB), verumontanum (VM), urethra entry into the prostate at apex (UA), as well as centroids of cysts and calcifications. UB, UA and VM locations are annotated both in MR and US for all cases, while cysts and calcifications are annotated when applicable. Fiducial points are stored in comma-separated CSV-style format adopted by 3D Slicer software (.fcsv file extension). There is one row per point in these files, encoding the location of the point in RAS coordinate space relative to the image data, and the name of the point.

Viewing the collection

We tested visualization of images, segmentations and fiducials in 3D Slicer software, and thus recommend 3D Slicer as the platform for visualization. 3D Slicer is a free open source platform (see http://slicer.org), with the pre-compiled binaries available for all major operating systems. You can download 3D Slicer at http://download.slicer.org.

Acknowledgments

Preparation of this data collection was made possible thanks to the funding from the National Institutes of Health (NIH) through grants R01 CA111288 and P41 RR019703.

If you use this dataset in a publication, please cite the following manuscript. You can also learn more about this dataset from the publication below.

Fedorov, A., Khallaghi, S., Antonio Sánchez, C., Lasso, A., Fels, S., Tuncali, K., Sugar, E. N., Kapur, T., Zhang, C., Wells, W., Nguyen, P. L., Abolmaesumi, P. & Tempany, C. Open-source image registration for MRI–TRUS fusion-guided prostate interventions. Int J CARS 10, 925–934 (2015). https://pubmed.ncbi.nlm.nih.gov/25847666/

Contact

Andrey Fedorov, fedorov@bwh.harvard.edu

This record contains raw data related to article “Assessing the Role of High-resolution Microultrasound Among Naïve Patients with Negative Multiparametric Magnetic Resonance Imaging and a Persistently High Suspicion of Prostate Cancer"

Abstract

Background: Multiparametric magnetic resonance imaging (mpMRI) is an invaluable diagnostic tool in the decision-making for prostate biopsies (PBx). However, a non-negligible proportion of patients with negative MRI (nMRI) may still harbour prostate cancer (PCa).

Objective: To assess whether microultrasound (micro-US) can help in substratifying the presence of PCa and clinically significant PCa (csPCa; ie, any Gleason score ≥7 PCa) in patients with nMRI despite a persistently high clinical suspicion of PCa.

Design setting and participants: A total of 125 biopsy-naïve patients who underwent micro-US-guided PBx with the ExactVu system for a persistently high suspicion of PCa despite nMRI were prospectively enrolled.

Intervention: The Prostate Risk Identification using micro-US (PRI-MUS) protocol was used to identify suspicious areas; PBx included targeted sampling of PRI-MUS ≥3 areas and systematic sampling.

Outcome measurements and statistical analysis: The primary endpoint was the assessment of micro-US diagnostic accuracy in detecting csPCa. Secondary endpoints included determining the proportion of patients with nMRI who may avoid PBx after micro-US or transrectal US, presence of cribriform and intraductal patterns on biopsy core examination, predictors of csPCa in patients presenting with nMRI, and comparing micro-US-targeted and systematic PBx in identifying csPCa.

Results and limitations: Considering csPCa detection rate, micro-US showed optimal sensitivity and negative predictive value (respectively, 97.1% and 96.4%), while specificity and positive predictive value were 29.7% and 34.0%, respectively. Twenty-eight (22.4%) patients with a negative micro-US examination could have avoided PBx with one (2.9%) missed csPCa. Cribriform and intraductal patterns were found in 14 (41.2%) and four (11.8%) of csPCa patients, respectively. In multivariable logistic regression models, positive micro-US, age, digital rectal examination, and prostate-specific antigen density ≥0.15 emerged as independent predictors of PCa. Targeted and systematic sampling identified 33 (97.1%) and 26 (76.5%) csPCa cases, respectively. The main limitation of the current study is represented by its retrospective single-centre nature on an operator-dependent technology.

Conclusions: Micro-US represents a valuable tool to rule out the presence of csPCa among patients with a persistent clinical suspicion despite nMRI.

Patient summary: According to our results, microultrasound (micro-US) may represent an effective tool for the diagnosis of clinically significant prostate cancer in patients with negative magnetic resonance imaging (nMRI), providing high sensitivity and negative predictive value. Further randomised studies are needed to confirm the potential role of micro-US in the diagnostic pathway of patients with a persistent suspicion of prostate cancer despite nMRI.

This record contains raw data related to article “Prospective evaluation of the role of imaging techniques and TMPRSS2:ERG mutation for the diagnosis of clinically significant prostate cancer"

Abstract

Objectives: To test the hypothesis of a relationship between a specific genetic lesion (T2:ERG) and imaging scores, such as PI-RADS and PRI-MUS, and to test the effectiveness of these parameters for the diagnosis of prostate cancer (PCa) and clinically significant PCa (csPCa).

Materials and methods: This is a prospective study of men with suspected PCa enrolled between 2016 and 2019 at a high-volume tertiary hospital. Patients underwent systematic US-guided biopsy, plus targeted biopsy if they were presenting with >=1 suspicious lesion (PI-RADS>2) at mpMRI or PR-IMUS >2 at micro-ultrasound assessment. For each patient, one core from the highest PI-RADS or PRI-MUS lesion was collected for T2:ERG analysis. Multivariable logistic regression models (LRMs) were fitted for csPCa with a clinical model (age, total PSA, previous biopsy, family history for PCa), a clinical plus PI-RADS, clinical plus T2:ERG, clinical plus PI-RADS plus T2:ERG, and T2:ERG plus PI-RADS alone.

Results: The cohort consists of 158 patients: 83.5% and 66.2% had respectively a diagnosis of PCa and csPCa after biopsy. A T2:ERG fusion was found in 37 men and 97.3% of these patients harbored PCa, while 81.1% were diagnosed with csPCa. SE of T2:ERG assay for csPCa was 28.8%, SP 87.0%, NPV 38.8%, and PPV 81.1%. Of 105 patients who performed mpMRI 93.% had PIRADS ≥3. SE of mpMRI for csPCa was 98.5%, SP was 12.8%, NPV was 83.3%, and PPV was 65.7%. Among 67 patients who were subjected to micro-US, 90% had a PRI-MUS ≥3. SE of micro-US for csPCa was 89.1%, SP was 9.52%, NPV was 28.6%, and PPV was 68.3%. At univariable LRM T2:ERG was confirmed as independent of mpMRI and micro-US result (OR 1.49, p=0.133 and OR 1.82, p=0.592, respectively). At multivariable LRM the clinical model alone had an AUC for csPCa of 0.74 while the clinical model including PI-RADS and T2:ERG achieved an AUC of 0.83.

Conclusions: T2:ERG translocation and imaging results are independent of each other, but both are related csPCa. To evaluate the best diagnostic work-up for PCa and csPCa detection, all available tools (T2:ERG detection and imaging techniques) should be employed together as they appear to have a complementary role.

Prostate cancer (PCa) tends to be more aggressive and lethal in African Americans (AA) compared to European Americans (EA). To further understand the biological factors accounting for the PCa disparities observed in AA and EA patients, we performed gene profiling analysis using Affymetrix human exon 1.0 ST arrays to identify the differentially expressed genes in AA and EA patients. 35 prostate biopsy specimens (tumor and adjacent normal tissues) were collected from 20 African American and 15 European American prostate cancer patients. RNA samples, purified from the collected biopy specimens, were process and applied to Affymetrix human exon ST 1.0 arrays. Array data was normalized, batch corrected and analyzed (1-way ANOVA) using Partek Genomics Suite program.

The integration of diverse ‘omic’ datasets will increase our understanding of the key signaling pathways that drive disease. Here, we used clinical tissue cohorts corresponding to lethal metastatic castration resistant prostate cancer (CRPC) obtained at rapid autopsy to integrate mutational, transcriptomic, and phosphoproteomic datasets for pathway analysis. Using Tied Diffusion through Interacting Events (TieDIE), we integrated differentially expressed transcriptional master regulators, functionally mutated genes, and differentially ‘activated’ kinases in CRPC tissues to synthesize a robust signaling network consisting of pathways with known and novel gene interactions. For 6 individual CRPC patients for which we had transcriptomic and phosphoproteomic data we observed distinct pathway activation states for each patient profile. In one patient, the activated pathways were strikingly similar to a prostate cancer cell line, 22Rv1, providing us with a good pre-clinical model to test targeted, combination therapies. In all, synthesis of multiple ‘omic’ datasets revealed a plethora of pathway information suitable for targeted therapies in lethal prostate cancer.

Prostate cancer (PCa) tends to be more aggressive and lethal in African Americans (AA) compared to European Americans (EA). To further understand the thebiological risk factors associated with PCa disparities observed in AA and EA patients, we performed microRNA profiling using Agilent Human miRNA arrays to identify the differentially expressed microRNAs beween: 1) AA and EA PCa patients; 2) AA PCa vs. AA normal; and 3) EA PCa vs. EA normal. 54 prostate biopsy specimens (tumor and adjacent normal tissues) were collected from 14 African American and 13 European American prostate cancer patients. 54 RNA samples, purified from the collected biopy specimens using Qiagen miRNeasy kit, were process and applied to Agilent human miRNA arrays. Array data was normalized and analyzed using Agilent GeneSpring program.

This is a dataset from the original publication “Reasons for missing clinically significant prostate cancer by targeted magnetic resonance imaging/ultrasound fusion-guided biopsy”. From 01/2014 to 04/2019 a sample collective of 785 patients with 3T multiparametric magnetic resonance imaging (mp-MRI) of the prostate and subsequent combined systematic biopsy (SB) and magnetic resonance imaging/ultrasound (US) fusion-guided biopsy (TB) was retrospectively analyzed. Prostate carcinoma (PCa) detection by TB and/or additional SB was analyzed. Related research article: Klingebiel M., Arsov C., Ullrich T., Quentin M., Al-Monajjed R., Mally D., Sawicki L.M., Hiester A., Esposito I., Albers P., Antoch G., Schimmöller L., Reasons for missing clinically significant prostate cancer by targeted magnetic resonance imaging/ultrasound fusion-guided biopsy, Eur J Radiol. 2021 Apr;137:109587. DOI: 10.1016/j.ejrad.2021.109587

The analysis of research data plays a key role in data-driven areas of science. Varieties of mixed research data sets exist and scientists aim to derive or validate hypotheses to find undiscovered knowledge. Many analysis techniques identify relations of an entire dataset only. This may level the characteristic behavior of different subgroups in the data. Like automatic subspace clustering, we aim at identifying interesting subgroups and attribute sets. We present a visual-interactive system that supports scientists to explore interesting relations between aggregated bins of multivariate attributes in mixed data sets. The abstraction of data to bins enables the application of statistical dependency tests as the measure of interestingness. An overview matrix view shows all attributes, ranked with respect to the interestingness of bins. Complementary, a node-link view reveals multivariate bin relations by positioning dependent bins close to each other. The system supports information drill-down based on both expert knowledge and algorithmic support. Finally, visual-interactive subset clustering assigns multivariate bin relations to groups. A list-based cluster result representation enables the scientist to communicate multivariate findings at a glance. We demonstrate the applicability of the system with two case studies from the earth observation domain and the prostate cancer research domain. In both cases, the system enabled us to identify the most interesting multivariate bin relations, to validate already published results, and, moreover, to discover unexpected relations.

These are three datasets of esophageal, colorectal and prostate cancer which included information of sociodemographic characteristics of patients, sign and symptom of presentation, imaging and histological characteristics, and TNM stage of cancer. Furthermore, the datasets also contain the type of diagnostic work-up and treatment options (surgery, chemotherapy, and radiotherapy) a patient received. Furthermore, the event status (whether the patient died or not) and the date of the event is also available.

Lists of abbreviations and acronyms (variables) used in the dataset.

Adj_organ- Adjacent organ involvement ADT - Androgen Deprivation Therapy Ba - Barium CEA - Carcinoembryonic antigen CT scan - Computed tomography scan CXR - Chest x-ray DATEDX - Date of Diagnosis Diff_swallowing- Difficulty of swallowing Dist_metastasis- Distant metastasis DM - Diabetes Mellitus FU - Follow-up Hist_grade - Histologic grade Hist_type - Histologic type Hgb - Hemoglobin HTN - Hypertension LN - Lymph Node Mstatus - Marital status PSA - Prostate Specific Antigen TNM - Tumor, Nodes, and Metastases T_Hital_eso - Transhiatal esophagectomy T_Thoracic_eso- Transthoracic esophagectomy US - Ultrasound Vstatus - Vital status

BackgroundIn terms of medical costs, prostate cancer is on the increase as one of the most costly cancers, posing a tremendous economic burden, but evidence on the health care utilization and medical expenditure of prostate cancer has been absent in China.ObjectiveThis study aimed to analyze health care utilization and direct medical costs of patients with prostate cancer in China.MethodsHealth care service data with a national representative sample of basic medical insurance beneficiaries between 2015 and 2017 were obtained from the China Health Insurance Association database. We conducted descriptive and statistical analyses of health care utilization, annual direct medical costs, and composition based on cancer-related medical records. Health care utilization was measured by the number of hospital visits and the length of stay.ResultsA total of 3,936 patients with prostate cancer and 24,686 cancer-related visits between 2015 and 2017 were identified in the database. The number of annual outpatient and inpatient visits per patient differed significantly from 2015 to 2017. There was no obvious change in length of stay and annual direct medical costs from 2015 to 2017. The number of annual visits per patient (outpatient: 3.0 vs. 4.0, P < 0.01; inpatient: 1.5 vs. 2.0, P < 0.001) and the annual medical direct costs per patient (US$2,300.1 vs. US$3,543.3, P < 0.001) of patients covered by the Urban Rural Resident Basic Medical Insurance (URRBMI) were both lower than those of patients covered by the Urban Employee Basic Medical Insurance (UEBMI), and the median out-of-pocket expense of URRBMI was higher than that of UEBMI (US$926.6 vs. US$594.0, P < 0.001). The annual direct medical costs of patients with prostate cancer in Western regions were significantly lower than those of patients in Eastern and Central regions (East: US$4011.9; Central: US$3458.6; West: US$2115.5) (P < 0.001).ConclusionsThere was an imbalanced distribution of health care utilization among regions in China. The direct medical costs of Chinese patients with prostate cancer remained stable, but the gap in health care utilization and medical costs between two different insurance schemes and among regions still needed to be further addressed.

UroLlmEvalSet

  Content

This dataset contains 149 short textual descriptions in German medical language about the diagnosis and medical history of patients with prostate cancer. The prostate cancer diagnosis is not present in all of the texts and many texts also contain other tumor diagnoses. The texts are labeled with the three-character ICD-10 codes of the diagnoses and with the date of the initial diagnosis of the tumor. Some diagnosis dates in the text are specified… See the full description on the dataset page: https://huggingface.co/datasets/stefan-m-lenz/UroLlmEvalSet.

Objective: To assess the extent of early mortality and its temporal course after prostatectomy and radiotherapy in the general population.Methods: Data from the Surveillance, Epidemiology, and End Results (SEER) database and East German epidemiologic cancer registries were used for the years 2005–2013. Metastasized cases were excluded. Analyzing overall mortality, year-specific Cox regression models were used after adjusting for age (including age squared), risk stage, and grading. To estimate temporal hazards, we computed year-specific conditional hazards for surgery and radiotherapy after propensity-score matching and applied piecewise proportional hazard models.Results: In German and US populations, we observed higher initial 3-month mortality odds for prostatectomy (USA: 9.4, 95% CI: 7.8–11.2; Germany: 9.1, 95% CI: 5.1–16.2) approaching the null effect value not before 24-months (estimated annual mean 36-months in US data) after diagnosis. During the observational period, we observed a constant hazard ratio for the 24-month mortality in the US population (2005: 1.7, 95% CI: 1.5–1.9; 2013: 1.9, 95% CI: 1.6–2.2) comparing surgery and radiotherapy. The same was true in the German cohort (2005: 1.4, 95% CI: 0.9–2.1; 2013: 3.3, 95% CI: 2.2–5.1). Considering low-risk cases, the adverse surgery effect appeared stronger.Conclusion: There is strong evidence from two independent populations of a considerably higher early to midterm mortality after prostatectomy compared to radiotherapy extending the time of early mortality considered by previous studies up to 36-months.

Substantial geographic variation in healthcare practices exist. Active surveillance (AS) has emerged as a critical tool in the management of men with low-risk prostate cancer. Whether there have been regional differences in adoption is largely unknown. The SEER “Prostate with Watchful Waiting Database” was used to identify patients diagnosed with localized low-risk prostate cancer and managed with AS across US census regions between 2010 and 2016. Multivariable logistic regression models were used to determine the impact of region on undergoing AS and factors associated with AS use within each US census region. Between 2010 and 2016, the proportion of men managed with AS increased from 20.8% to 55.9% in the West, 11.5% to 50.0% in Northeast, 9.9% to 43.4% in the South and 15.1% to 56.2% in Midwest (p < 0.0001). On multivariable analysis, as compared to the West, men in all regions were less likely to undergo AS (p < 0.001). Black men in the West (OR 1.36, 95%CI 1.25–1.49) and Midwest (OR 1.62, 95%CI 1.35–1.95) were more likely to undergo AS, but less likely in Northeast (OR 0.80, 95%CI 0.69–0.92). Men with higher socioeconomic status (SES) were more likely to undergo AS in the West (OR 1.47, 95%CI 1.39–1.55), Northeast (OR 1.57, 95%CI 1.36–1.81), and South (OR 1.24, 95%CI 1.13–1.37) but not in the Midwest (OR 0.85, 95%CI 0.73–0.98). We found striking regional differences in the uptake of AS according to race and SES. Geography must be taken into consideration when assessing barriers to AS use.

BackgroundDelays in time to treatment initiation (TTI) for new cancer diagnoses cause patient distress and may adversely affect outcomes. We investigated trends in TTI for common solid tumors treated with curative intent, determinants of increased TTI and association with overall survival.Methods and findingsWe utilized prospective data from the National Cancer Database for newly diagnosed United States patients with early-stage breast, prostate, lung, colorectal, renal and pancreas cancers from 2004–13. TTI was defined as days from diagnosis to first treatment (surgery, systemic or radiation therapy). Negative binomial regression and Cox proportional hazard models were used for analysis. The study population of 3,672,561 patients included breast (N = 1,368,024), prostate (N = 944,246), colorectal (N = 662,094), non-small cell lung (N = 363,863), renal (N = 262,915) and pancreas (N = 71,419) cancers. Median TTI increased from 21 to 29 days (P