Evidence map›Paper›PMID 42637010›Full record

ArticleModern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc2026

Radiologic, Pathologic, and Deep Learning Predictors of Response to Immune Checkpoint Blockade in Renal Cell Carcinoma Patients Undergoing Post-Treatment Nephrectomy.

Payal Kapur, Alana Christie, Vipul Jarmale, Farzad Tahmasebi, Bingqing Xie, Jeffrey Miyata, Dinesh Rakheja, Jeffrey A Cadeddu, Vitaly Margulis, Hans Hammers and 4 more

Abstract read
PubMed Publisher
In one paragraph

Article in Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

Payal KapurDepartment of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas; Department of Urology, University of Texas Southwestern Medical Center, Dallas, Texas; Department of Kidney Cancer Program at Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas. Electronic address: payal.kapur@utsouthwestern.edu.
Alana ChristieDepartment of Kidney Cancer Program at Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas.
Vipul JarmaleDepartment of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, Texas.
Farzad TahmasebiDepartment of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas.
Bingqing XieDepartment of Kidney Cancer Program at Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas.
Jeffrey MiyataDepartment of Kidney Cancer Program at Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas.
Dinesh RakhejaDepartment of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas.
Jeffrey A CadedduDepartment of Urology, University of Texas Southwestern Medical Center, Dallas, Texas; Department of Kidney Cancer Program at Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas.
Vitaly MargulisDepartment of Urology, University of Texas Southwestern Medical Center, Dallas, Texas; Department of Kidney Cancer Program at Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas.
Hans HammersHematology-Oncology Division of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas.
Tian ZhangHematology-Oncology Division of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas.
Ivan PedrosaDepartment of Kidney Cancer Program at Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas; Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas.
James BrugarolasDepartment of Kidney Cancer Program at Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas; Hematology-Oncology Division of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas.
Satwik RajaramDepartment of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, Texas. Electronic address: satwik.rajaram@utsouthwestern.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Response assessment of primary kidney tumors in the consolidation cytoreductive and neoadjuvant settings offers a unique opportunity to inform postoperative adaptive treatment strategies. Yet, systematic analyses of nephrectomy specimens post immune checkpoint inhibition (ICI) therapies are lacking. We retrospectively identified consecutive renal cell carcinoma (RCC) patients with locoregionally advanced or metastatic RCC who received at least one cycle of ICI-containing doublet therapy prior to nephrectomy at the UTSW Kidney Cancer Program (2017-2024). Radiologic and pathologic features were centrally reviewed and correlated with clinical outcomes: freedom from start of next systemic therapy (FFNT) in cytoreductive patients and metastasis-free survival (MFS) in neoadjuvant patients. Pathologic response to ICI results in tumor cell death and fibrosis, creating hypocellular areas and increased immune infiltrate, features we utilized to develop Deep learning (DL) models. We leveraged these DL models to provide an objective quantitative complement to pathologist-assessed regression and to quantitate immune infiltrate. Among 99 patients (cytoreductive nephrectomy {CN}, n=66; neoadjuvant nephrectomy {NaN}, n=33), radiologic tumor shrinkage ≥30% (p=0.0036) and the extent of ICI-induced pathologic regression as assessed by central pathology review (HR 0.97; CI 0.95-0.99; p=0.0023), were significantly associated with prolonged FFNT, with similar trends in neoadjuvant cohort. Presence of coagulative tumor necrosis continued to be associated with poor outcomes. These findings were concordant using a DL-based quantitative assessment (HR 0.96; CI 0.93-0.99; p=0.0041). In exploratory multivariable Cox regression, pathologic regression, DL-derived extent of immune infiltrate, and largest tumor dimension at nephrectomy remained associated with FFNT. This study provides, to our knowledge, the first integrated, quantitative assessment of radiologic, pathologic, and image-based response metrics following preoperative ICI therapy in RCC. If validated prospectively, these findings may help guide adaptive treatment approaches and clinical trial design.

Indexed as

ccRCCclear cell renal cell carcinomacytoreductive therapyimmunotherapyneoadjuvantpredictive biomarkersprognosisrenal cell carcinoma

Identifiers

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.