Reetesh K. Pai
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View article: Supplementary Table S4 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence
Supplementary Table S4 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence Open
Supplementary Table S4
View article: Supplementary Table S1 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence
Supplementary Table S1 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence Open
Supplementary Table S1
View article: FIGURE 2 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence
FIGURE 2 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence Open
Artificial intelligence–derived morphologic features in stage III colon cancers (NCCTG N0147 trial) that were found to be most strongly associated with patient TTR. Data are shown in Kaplan–Meier plots for TSR among p-MMR tumors in initial…
View article: TABLE 2 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence
TABLE 2 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence Open
Multivariable models for the prediction of TTR by morphologic features and clinicopathological variables in initial cohort [p-MMR (n = 189) or d-MMR (n = 191) stage III colon cancers]
View article: TABLE 3 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence
TABLE 3 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence Open
Multivariable models for the prediction of TTR by morphologic features and clinicopathological variables in the validation cohort [p-MMR (n = 1,094) or d-MMR (n = 176) stage III colon cancers]
View article: Supplementary Table S3 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence
Supplementary Table S3 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence Open
Supplementary Table S3
View article: Data from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence
Data from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence Open
Deep learning may detect biologically important signals embedded in tumor morphologic features that confer distinct prognoses. Tumor morphologic features were quantified to enhance patient risk stratification within DNA mismatch repair (MM…
View article: FIGURE 2 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence
FIGURE 2 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence Open
Artificial intelligence–derived morphologic features in stage III colon cancers (NCCTG N0147 trial) that were found to be most strongly associated with patient TTR. Data are shown in Kaplan–Meier plots for TSR among p-MMR tumors in initial…
View article: TABLE 2 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence
TABLE 2 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence Open
Multivariable models for the prediction of TTR by morphologic features and clinicopathological variables in initial cohort [p-MMR (n = 189) or d-MMR (n = 191) stage III colon cancers]
View article: FIGURE 1 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence
FIGURE 1 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence Open
QuantCRC detects tumor morphologic features in four layers. TB/PDC, tumor budding/poorly differentiated cluster; TIL, tumor-infiltrating lymphocytes. https://cloud.aiforia.com/Public/MayoUpmcAiforia_Pai/0z9TK9WQComQSW5MOEo_1KieA8U9KX9oCFbc…
View article: Supplementary Table S2 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence
Supplementary Table S2 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence Open
Supplementary Table S2
View article: Supplementary Table S3 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence
Supplementary Table S3 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence Open
Supplementary Table S3
View article: Supplementary Table S2 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence
Supplementary Table S2 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence Open
Supplementary Table S2
View article: TABLE 1 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence
TABLE 1 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence Open
DL-derived tumor morphologic features by DNA MMR status
View article: FIGURE 1 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence
FIGURE 1 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence Open
QuantCRC detects tumor morphologic features in four layers. TB/PDC, tumor budding/poorly differentiated cluster; TIL, tumor-infiltrating lymphocytes. https://cloud.aiforia.com/Public/MayoUpmcAiforia_Pai/0z9TK9WQComQSW5MOEo_1KieA8U9KX9oCFbc…
View article: Supplementary Table S1 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence
Supplementary Table S1 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence Open
Supplementary Table S1
View article: Data from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence
Data from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence Open
Deep learning may detect biologically important signals embedded in tumor morphologic features that confer distinct prognoses. Tumor morphologic features were quantified to enhance patient risk stratification within DNA mismatch repair (MM…
View article: TABLE 3 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence
TABLE 3 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence Open
Multivariable models for the prediction of TTR by morphologic features and clinicopathological variables in the validation cohort [p-MMR (n = 1,094) or d-MMR (n = 176) stage III colon cancers]
View article: Supplementary Table S4 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence
Supplementary Table S4 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence Open
Supplementary Table S4
View article: TABLE 1 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence
TABLE 1 from Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence Open
DL-derived tumor morphologic features by DNA MMR status
View article: Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence
Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence Open
Deep learning may detect biologically important signals embedded in tumor morphologic features that confer distinct prognoses. Tumor morphologic features were quantified to enhance patient risk stratification within DNA mismatch repair (MM…
View article: Supplementary Figure S1 from Improved Risk-Stratification Scheme for Mismatch-Repair Proficient Stage II Colorectal Cancers Using the Digital Pathology Biomarker QuantCRC
Supplementary Figure S1 from Improved Risk-Stratification Scheme for Mismatch-Repair Proficient Stage II Colorectal Cancers Using the Digital Pathology Biomarker QuantCRC Open
Supplementary Figure S1. Flow diagram outlining the steps undertaken in this study.
View article: Supplementary Table S2 from Improved Risk-Stratification Scheme for Mismatch-Repair Proficient Stage II Colorectal Cancers Using the Digital Pathology Biomarker QuantCRC
Supplementary Table S2 from Improved Risk-Stratification Scheme for Mismatch-Repair Proficient Stage II Colorectal Cancers Using the Digital Pathology Biomarker QuantCRC Open
Supplementary Table S2. Concordance between ASCO and QuantCRC-Integrated risk groups
View article: Supplementary Figure S2 from Improved Risk-Stratification Scheme for Mismatch-Repair Proficient Stage II Colorectal Cancers Using the Digital Pathology Biomarker QuantCRC
Supplementary Figure S2 from Improved Risk-Stratification Scheme for Mismatch-Repair Proficient Stage II Colorectal Cancers Using the Digital Pathology Biomarker QuantCRC Open
Supplementary Figure S2. Scatter plots of QuantCRC features stratified by QuantCRC-integrated and ASCO risk schemes.
View article: Supplementary Table S1 from Improved Risk-Stratification Scheme for Mismatch-Repair Proficient Stage II Colorectal Cancers Using the Digital Pathology Biomarker QuantCRC
Supplementary Table S1 from Improved Risk-Stratification Scheme for Mismatch-Repair Proficient Stage II Colorectal Cancers Using the Digital Pathology Biomarker QuantCRC Open
Supplementary Table S1. Univariate and multivariate analysis of QuantCRC risk classification, pT stage, and any high-risk pathologic feature.
View article: Supplementary Table S5 from Improved Risk-Stratification Scheme for Mismatch-Repair Proficient Stage II Colorectal Cancers Using the Digital Pathology Biomarker QuantCRC
Supplementary Table S5 from Improved Risk-Stratification Scheme for Mismatch-Repair Proficient Stage II Colorectal Cancers Using the Digital Pathology Biomarker QuantCRC Open
Supplementary Table S5. Adjuvant chemotherapy according to ASCO and QuantCRC-Integrated risk groups.
View article: Supplementary Table S4 from Improved Risk-Stratification Scheme for Mismatch-Repair Proficient Stage II Colorectal Cancers Using the Digital Pathology Biomarker QuantCRC
Supplementary Table S4 from Improved Risk-Stratification Scheme for Mismatch-Repair Proficient Stage II Colorectal Cancers Using the Digital Pathology Biomarker QuantCRC Open
Supplementary Table S4. Clinical and Pathologic features in patients that were concordant or discordant in the ASCO and QuantCRC-Integrated schemes.
View article: Supplementary Table S3 from Improved Risk-Stratification Scheme for Mismatch-Repair Proficient Stage II Colorectal Cancers Using the Digital Pathology Biomarker QuantCRC
Supplementary Table S3 from Improved Risk-Stratification Scheme for Mismatch-Repair Proficient Stage II Colorectal Cancers Using the Digital Pathology Biomarker QuantCRC Open
Supplementary Table S3. Carcinoembryonic antigen levels according to ASCO and QuantCRC-Integrated risk groups.
View article: Data from Improved Risk-Stratification Scheme for Mismatch-Repair Proficient Stage II Colorectal Cancers Using the Digital Pathology Biomarker QuantCRC
Data from Improved Risk-Stratification Scheme for Mismatch-Repair Proficient Stage II Colorectal Cancers Using the Digital Pathology Biomarker QuantCRC Open
Purpose:There is a need to improve current risk stratification of stage II colorectal cancer to better inform risk of recurrence and guide adjuvant chemotherapy. We sought to examine whether integration of QuantCRC, a digital pathology bio…
View article: Supplementary Table S3 from Improved Risk-Stratification Scheme for Mismatch-Repair Proficient Stage II Colorectal Cancers Using the Digital Pathology Biomarker QuantCRC
Supplementary Table S3 from Improved Risk-Stratification Scheme for Mismatch-Repair Proficient Stage II Colorectal Cancers Using the Digital Pathology Biomarker QuantCRC Open
Supplementary Table S3. Carcinoembryonic antigen levels according to ASCO and QuantCRC-Integrated risk groups.