cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7629109
title: Epidote - Sample 1 NanED Round Robin, Data: ESR12

description: <strong><em>Epidote</em></strong> The following submission contains the data collection and processing of datasets for the sample epidote under the NanEd round-robin project. A single crystal was identified, and precession data acquisition techniques were used to collect datasets on the same crystal. The data sets were processed with PETS2 software. The table below summarizes the data collection parameters for the data sets. The following data is also included as a text file in the data folder. <strong>Precession:</strong> <strong>General information:</strong> Project NanED (www.naned.eu) ESR Project ESR12 - Round Robin Project Label RR1 Sample Label RR1-S1 Data set Label RR1-sample1-PEDT <strong>Instrumental:</strong> Instrument Transmission electron microscope Jeol F200 Radiation source Cold FEG Accelerating voltage 200 kV Wavelength 0.0251 Å Probe Type Microdiffraction Beam Diameter 75nm Beam Convergence Parallel beam, convergence &lt;0.1mrad Detector Hybrid pixel detector ASI Cheetah M3 (bottom mounted) Number of pixels in the image 512 x 512 Pixel size 55 µm x 55 µm Effective camera length 200 mm Calibration constant 0.00720 Å<sup>-1</sup>/pixel <strong>Sample description:</strong> Name Epidote Chemical composition Ca<sub>2</sub>Fe<sub>x</sub>Al<sub>3-x</sub>Si<sub>3</sub>O<sub>13</sub>H Sample source Natural source from Val d'Ossola, Italy Sample preparation Powder crushed on a mortar and dispersed in n-butanol, drop deposited on a Cu grid with holey C film <strong>Experimental:</strong> Data Type Electron diffraction data - 3D ED Data collection method PEDT Temperature (K) used during data collection 293 K Number of crystals contributing to the data set 1 Number of experimental frames 95 tilt range, tilt step, tilt per frame -49.4° to +61.6°, 1°, 0° Precession angle 1.25° Exposure time per frame 500ms <strong>Software:</strong> Software used for the data collection Instamatic software Software used for processing PETS2 (ver 2.1.20211012.1037) <strong>Authorship and bibliography</strong> Author(s) of the data Sara Passuti (ESR12) Related data Publication(s) <strong>Files and data formats</strong> Image folder tiff : Folder containing images of the diffraction pattern from each frames. Image format tiff_16bit Additional folders/files sample1_crystal_image : image of the crystal sample1-exp4_petsdata : Log files of PETS2 processing sample1-exp4.pts2 :_input file for the program PETS2 used for processing the data <strong>Notes:</strong> *Project Label "RR1" stands for Round Robin 1 *Data set label "RR1-sample-PEDT" stands for Round Robin 1 sample 1, data collection method Precession. *The additional files can be found in the folder: Precession\Data_collection_processing
DOI: 10.5281/zenodo.7412529
title: Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-CD3/CD20 (lymphocytes)

description: <strong>LICENSE</strong> This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (<strong>CC-BY-NC-SA 4.0</strong>) For non-commercial use, please use the dataset under CC-BY-NC-SA.<br> If you would like to use the dataset for commercial purposes, please contact us (ishum-prm@m.u-tokyo.ac.jp). A Tar.gz file contains the following files: - HE image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_HE.png - Mask image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_mask.png Each image file is 984x984 px. posX and posY are the leftmost position in WSI coordinate. Mask files store binary segmentation mask (background : 0, target : 1) A csv file contains the following information: antigen : Antibodies for this antigen were used to create the segmentation mask. filename: filename of image or mask file. train_val_test : train, validation, or test sample in the paper. <strong>Citation</strong> If you use this dataset for your research, please cite our paper. Daisuke Komura, Takumi Onoyama, Koki Shinbo, Hiroto Odaka, Minako Hayakawa, Mieko Ochi, Ranny Rahaningrum Herdiantoputri, Haruya Endo, Hiroto Katoh, Tohru Ikeda, Tetsuo Ushiku, Shumpei Ishikawa,<br> Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists, Patterns, Volume 4, Issue 2, 2023, 100688, https://doi.org/10.1016/j.patter.2023.100688.
DOI: 10.5281/zenodo.6456709
title: Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-CD3/CD20 (lymphocytes)

description: <strong>LICENSE</strong> This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (<strong>CC-BY-NC-SA 4.0</strong>) For non-commercial use, please use the dataset under CC-BY-NC-SA.<br> If you would like to use the dataset for commercial purposes, please contact us (ishum-prm@m.u-tokyo.ac.jp). A Tar.gz file contains the following files: - HE image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_HE.png - Mask image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_mask.png Each image file is 984x984 px. posX and posY are the leftmost position in WSI coordinate. Mask files store binary segmentation mask (background : 0, target : 1) A csv file contains the following information: antigen : Antibodies for this antigen were used to create the segmentation mask. filename: filename of image or mask file. train_val_test : train, validation, or test sample in the paper. <strong>Citation</strong> If you use this dataset for your research, please cite our paper. Daisuke Komura, Takumi Onoyama, Koki Shinbo, Hiroto Odaka, Minako Hayakawa, Mieko Ochi, Ranny Rahaningrum Herdiantoputri, Haruya Endo, Hiroto Katoh, Tohru Ikeda, Tetsuo Ushiku, Shumpei Ishikawa,<br> Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists, Patterns, Volume 4, Issue 2, 2023, 100688, https://doi.org/10.1016/j.patter.2023.100688.
DOI: 10.5281/zenodo.7412739
title: Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-CD45RB (leukocytes)

description: <strong>LICENSE</strong> This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (<strong>CC-BY-NC-SA 4.0</strong>) For non-commercial use, please use the dataset under CC-BY-NC-SA.<br> If you would like to use the dataset for commercial purposes, please contact us (ishum-prm@m.u-tokyo.ac.jp). A Tar.gz file contains the following files: - HE image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_HE.png - Mask image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_mask.png Each image file is 984x984 px. posX and posY are the leftmost position in WSI coordinate. Mask files store binary segmentation mask (background : 0, target : 1) A csv file contains the following information: antigen : Antibodies for this antigen were used to create the segmentation mask. filename: filename of image or mask file. train_val_test : train, validation, or test sample in the paper. <strong>Citation</strong> If you use this dataset for your research, please cite our paper. Daisuke Komura, Takumi Onoyama, Koki Shinbo, Hiroto Odaka, Minako Hayakawa, Mieko Ochi, Ranny Rahaningrum Herdiantoputri, Haruya Endo, Hiroto Katoh, Tohru Ikeda, Tetsuo Ushiku, Shumpei Ishikawa,<br> Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists, Patterns, Volume 4, Issue 2, 2023, 100688, https://doi.org/10.1016/j.patter.2023.100688.
DOI: 10.5281/zenodo.6456711
title: Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-CD45RB (leukocytes)

description: <strong>LICENSE</strong> This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (<strong>CC-BY-NC-SA 4.0</strong>) For non-commercial use, please use the dataset under CC-BY-NC-SA.<br> If you would like to use the dataset for commercial purposes, please contact us (ishum-prm@m.u-tokyo.ac.jp). A Tar.gz file contains the following files: - HE image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_HE.png - Mask image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_mask.png Each image file is 984x984 px. posX and posY are the leftmost position in WSI coordinate. Mask files store binary segmentation mask (background : 0, target : 1) A csv file contains the following information: antigen : Antibodies for this antigen were used to create the segmentation mask. filename: filename of image or mask file. train_val_test : train, validation, or test sample in the paper. <strong>Citation</strong> If you use this dataset for your research, please cite our paper. Daisuke Komura, Takumi Onoyama, Koki Shinbo, Hiroto Odaka, Minako Hayakawa, Mieko Ochi, Ranny Rahaningrum Herdiantoputri, Haruya Endo, Hiroto Katoh, Tohru Ikeda, Tetsuo Ushiku, Shumpei Ishikawa,<br> Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists, Patterns, Volume 4, Issue 2, 2023, 100688, https://doi.org/10.1016/j.patter.2023.100688.
DOI: 10.5281/zenodo.7412580
title: Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-CD235a (red blood cells)

description: <strong>LICENSE</strong> This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (<strong>CC-BY-NC-SA 4.0</strong>) For non-commercial use, please use the dataset under CC-BY-NC-SA.<br> If you would like to use the dataset for commercial purposes, please contact us (ishum-prm@m.u-tokyo.ac.jp). A Tar.gz file contains the following files: - HE image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_HE.png - Mask image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_mask.png Each image file is 984x984 px. posX and posY are the leftmost position in WSI coordinate. Mask files store binary segmentation mask (background : 0, target : 1) A csv file contains the following information: antigen : Antibodies for this antigen were used to create the segmentation mask. filename: filename of image or mask file. train_val_test : train, validation, or test sample in the paper. <strong>Citation</strong> If you use this dataset for your research, please cite our paper. Daisuke Komura, Takumi Onoyama, Koki Shinbo, Hiroto Odaka, Minako Hayakawa, Mieko Ochi, Ranny Rahaningrum Herdiantoputri, Haruya Endo, Hiroto Katoh, Tohru Ikeda, Tetsuo Ushiku, Shumpei Ishikawa,<br> Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists, Patterns, Volume 4, Issue 2, 2023, 100688, https://doi.org/10.1016/j.patter.2023.100688.
DOI: 10.5281/zenodo.6451382
title: Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-CD235a (red blood cells)

description: <strong>LICENSE</strong> This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (<strong>CC-BY-NC-SA 4.0</strong>) For non-commercial use, please use the dataset under CC-BY-NC-SA.<br> If you would like to use the dataset for commercial purposes, please contact us (ishum-prm@m.u-tokyo.ac.jp). A Tar.gz file contains the following files: - HE image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_HE.png - Mask image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_mask.png Each image file is 984x984 px. posX and posY are the leftmost position in WSI coordinate. Mask files store binary segmentation mask (background : 0, target : 1) A csv file contains the following information: antigen : Antibodies for this antigen were used to create the segmentation mask. filename: filename of image or mask file. train_val_test : train, validation, or test sample in the paper. <strong>Citation</strong> If you use this dataset for your research, please cite our paper. Daisuke Komura, Takumi Onoyama, Koki Shinbo, Hiroto Odaka, Minako Hayakawa, Mieko Ochi, Ranny Rahaningrum Herdiantoputri, Haruya Endo, Hiroto Katoh, Tohru Ikeda, Tetsuo Ushiku, Shumpei Ishikawa,<br> Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists, Patterns, Volume 4, Issue 2, 2023, 100688, https://doi.org/10.1016/j.patter.2023.100688.
DOI: 10.5281/zenodo.7412732
title: Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-αSMA (smooth muscle cells / cancer associated firbroblasts)

description: <strong>LICENSE</strong> This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (<strong>CC-BY-NC-SA 4.0</strong>) For non-commercial use, please use the dataset under CC-BY-NC-SA.<br> If you would like to use the dataset for commercial purposes, please contact us (ishum-prm@m.u-tokyo.ac.jp). A Tar.gz file contains the following files: - HE image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_HE.png - Mask image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_mask.png Each image file is 984x984 px. posX and posY are the leftmost position in WSI coordinate. Mask files store binary segmentation mask (background : 0, target : 1) A csv file contains the following information: antigen : Antibodies for this antigen were used to create the segmentation mask. filename: filename of image or mask file. train_val_test : train, validation, or test sample in the paper. <strong>Citation</strong> If you use this dataset for your research, please cite our paper. Daisuke Komura, Takumi Onoyama, Koki Shinbo, Hiroto Odaka, Minako Hayakawa, Mieko Ochi, Ranny Rahaningrum Herdiantoputri, Haruya Endo, Hiroto Katoh, Tohru Ikeda, Tetsuo Ushiku, Shumpei Ishikawa,<br> Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists, Patterns, Volume 4, Issue 2, 2023, 100688, https://doi.org/10.1016/j.patter.2023.100688.
DOI: 10.5281/zenodo.6456721
title: Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-αSMA (smooth muscle cells / cancer associated firbroblasts)

description: <strong>LICENSE</strong> This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (<strong>CC-BY-NC-SA 4.0</strong>) For non-commercial use, please use the dataset under CC-BY-NC-SA.<br> If you would like to use the dataset for commercial purposes, please contact us (ishum-prm@m.u-tokyo.ac.jp). A Tar.gz file contains the following files: - HE image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_HE.png - Mask image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_mask.png Each image file is 984x984 px. posX and posY are the leftmost position in WSI coordinate. Mask files store binary segmentation mask (background : 0, target : 1) A csv file contains the following information: antigen : Antibodies for this antigen were used to create the segmentation mask. filename: filename of image or mask file. train_val_test : train, validation, or test sample in the paper. <strong>Citation</strong> If you use this dataset for your research, please cite our paper. Daisuke Komura, Takumi Onoyama, Koki Shinbo, Hiroto Odaka, Minako Hayakawa, Mieko Ochi, Ranny Rahaningrum Herdiantoputri, Haruya Endo, Hiroto Katoh, Tohru Ikeda, Tetsuo Ushiku, Shumpei Ishikawa,<br> Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists, Patterns, Volume 4, Issue 2, 2023, 100688, https://doi.org/10.1016/j.patter.2023.100688.
DOI: 10.5281/zenodo.7412731
title: Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-panCK (epithelial cells)

description: <strong>LICENSE</strong> This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (<strong>CC-BY-NC-SA 4.0</strong>) For non-commercial use, please use the dataset under CC-BY-NC-SA.<br> If you would like to use the dataset for commercial purposes, please contact us (ishum-prm@m.u-tokyo.ac.jp). A Tar.gz file contains the following files: - HE image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_HE.png - Mask image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_mask.png Each image file is 984x984 px. posX and posY are the leftmost position in WSI coordinate. Mask files store binary segmentation mask (background : 0, target : 1) A csv file contains the following information: antigen : Antibodies for this antigen were used to create the segmentation mask. filename: filename of image or mask file. train_val_test : train, validation, or test sample in the paper. <strong>Citation</strong> If you use this dataset for your research, please cite our paper. Daisuke Komura, Takumi Onoyama, Koki Shinbo, Hiroto Odaka, Minako Hayakawa, Mieko Ochi, Ranny Rahaningrum Herdiantoputri, Haruya Endo, Hiroto Katoh, Tohru Ikeda, Tetsuo Ushiku, Shumpei Ishikawa,<br> Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists, Patterns, Volume 4, Issue 2, 2023, 100688, https://doi.org/10.1016/j.patter.2023.100688.
DOI: 10.5281/zenodo.6456713
title: Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-panCK (epithelial cells)

description: <strong>LICENSE</strong> This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (<strong>CC-BY-NC-SA 4.0</strong>) For non-commercial use, please use the dataset under CC-BY-NC-SA.<br> If you would like to use the dataset for commercial purposes, please contact us (ishum-prm@m.u-tokyo.ac.jp). A Tar.gz file contains the following files: - HE image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_HE.png - Mask image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_mask.png Each image file is 984x984 px. posX and posY are the leftmost position in WSI coordinate. Mask files store binary segmentation mask (background : 0, target : 1) A csv file contains the following information: antigen : Antibodies for this antigen were used to create the segmentation mask. filename: filename of image or mask file. train_val_test : train, validation, or test sample in the paper. <strong>Citation</strong> If you use this dataset for your research, please cite our paper. Daisuke Komura, Takumi Onoyama, Koki Shinbo, Hiroto Odaka, Minako Hayakawa, Mieko Ochi, Ranny Rahaningrum Herdiantoputri, Haruya Endo, Hiroto Katoh, Tohru Ikeda, Tetsuo Ushiku, Shumpei Ishikawa,<br> Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists, Patterns, Volume 4, Issue 2, 2023, 100688, https://doi.org/10.1016/j.patter.2023.100688.
DOI: 10.23708/cvwfbi
title: Climatic dataset from PROINPA, Khipakhipani, La Paz, Bolivia, 2020

description: This dataset corresponds to climate data for the year 2020 at Fundación PROINPA, Khipakhipani, La Paz, Bolivia. The data includes temperature, relative humidity, atmospheric pressure in three separate CSV files with approximately one measurement every minute. The first column indicates the date and time in UTC and the second column the measurement made (column names are visible in the header). The measurements were made using a BME680 type sensor calibrated with a DS18B20 temperature probe, and connected to a Raspberry Pi. The dataset includes an R script to read the data and basic graphical visualizations. More information can be found here: https://pi2p.ird.fr/
DOI: 10.5281/zenodo.7628653
title: Beating Versus Arrested Heart Isolated Tricuspid Valve Surgery: Long-term Outcomes

description: Russo M, Di Mauro M, Saitto G, Lio A, Berretta P, Taramasso M, Scrofani R, Della Corte A, Sponga S, Greco E, Saccocci M, Calafiore A, Bianchi G, Leviner DB, Biondi A, Della Ratta E, Livi U, Sharoni E, Werner P, De Vincentiis C, Di Eusanio M, Kocher A, Antona C, Miraldi F, Troise G, Solinas M, Maisano F, Laufer G, Musumeci F, Andreas M. Beating Versus Arrested Heart Isolated Tricuspid Valve Surgery: Long-term Outcomes. Ann Thorac Surg. 2022 Feb;113(2):585-592. doi: 10.1016/j.athoracsur.2021.03.070. Epub 2021 Apr 5. PMID: 33831392. Abstract <strong>Background: </strong>Isolated tricuspid valve (TV) surgery is a rare procedure generally considered at high risk for perioperative mortality and poor long-term outcomes. Surgical treatment can be performed with either an arrested heart (AH) or beating heart (BH) technique. The aim of this study was to compare the outcomes of isolated tricuspid surgery with 2 different approaches. <strong>Methods: </strong>The Surgical-Tricuspid Study is a multicenter international retrospective study enrolling adult patients who underwent isolated TV procedures (n = 406; age 56 ± 16 years; 56% female) at 13 international sites. The AH and BH strategies were performed in 253 and 153 patients, respectively. Propensity score-matched analysis was used to compare groups. <strong>Results: </strong>After matching, 129 pairs were obtained and analyzed. The 30-day mortality rate was 6.2% versus 5.0% in the AH and BH groups, respectively (P = .9). The rates of acute renal failure requiring replacement therapy (10% versus 3%; P = .02) and stroke (1.6% versus 0%; P = .08) were numerically higher in the AH group. The 6-year survival rate was 67% ± 6% versus 78% ± 5% in the AH and BH groups, respectively (P = .18), whereas freedom from cardiac death was 75% ± 5% versus 84% ± 4% (P = .21). The 6-year composite cardiac end point of cardiac death and reoperation rate was 60% ± 9% versus 86% ± 5% (P = .024) comparing AH-TV replacement and BH-TV repair groups. <strong>Conclusions: </strong>Isolated TV surgery performed with a BH strategy is a safe option and resulted in a trend of increased long-term survival and freedom from reoperation compared with the standard AH technique. Patients undergoing BH valve repair had the best long-term outcome.
DOI: 10.5281/zenodo.7628652
title: Beating Versus Arrested Heart Isolated Tricuspid Valve Surgery: Long-term Outcomes

description: Russo M, Di Mauro M, Saitto G, Lio A, Berretta P, Taramasso M, Scrofani R, Della Corte A, Sponga S, Greco E, Saccocci M, Calafiore A, Bianchi G, Leviner DB, Biondi A, Della Ratta E, Livi U, Sharoni E, Werner P, De Vincentiis C, Di Eusanio M, Kocher A, Antona C, Miraldi F, Troise G, Solinas M, Maisano F, Laufer G, Musumeci F, Andreas M. Beating Versus Arrested Heart Isolated Tricuspid Valve Surgery: Long-term Outcomes. Ann Thorac Surg. 2022 Feb;113(2):585-592. doi: 10.1016/j.athoracsur.2021.03.070. Epub 2021 Apr 5. PMID: 33831392. Abstract <strong>Background: </strong>Isolated tricuspid valve (TV) surgery is a rare procedure generally considered at high risk for perioperative mortality and poor long-term outcomes. Surgical treatment can be performed with either an arrested heart (AH) or beating heart (BH) technique. The aim of this study was to compare the outcomes of isolated tricuspid surgery with 2 different approaches. <strong>Methods: </strong>The Surgical-Tricuspid Study is a multicenter international retrospective study enrolling adult patients who underwent isolated TV procedures (n = 406; age 56 ± 16 years; 56% female) at 13 international sites. The AH and BH strategies were performed in 253 and 153 patients, respectively. Propensity score-matched analysis was used to compare groups. <strong>Results: </strong>After matching, 129 pairs were obtained and analyzed. The 30-day mortality rate was 6.2% versus 5.0% in the AH and BH groups, respectively (P = .9). The rates of acute renal failure requiring replacement therapy (10% versus 3%; P = .02) and stroke (1.6% versus 0%; P = .08) were numerically higher in the AH group. The 6-year survival rate was 67% ± 6% versus 78% ± 5% in the AH and BH groups, respectively (P = .18), whereas freedom from cardiac death was 75% ± 5% versus 84% ± 4% (P = .21). The 6-year composite cardiac end point of cardiac death and reoperation rate was 60% ± 9% versus 86% ± 5% (P = .024) comparing AH-TV replacement and BH-TV repair groups. <strong>Conclusions: </strong>Isolated TV surgery performed with a BH strategy is a safe option and resulted in a trend of increased long-term survival and freedom from reoperation compared with the standard AH technique. Patients undergoing BH valve repair had the best long-term outcome.