Pembrolizumab, a humanized IgG4 monoclonal antibody targeting programmed death-1 protein, has demonstrated efficacy in relapsed/refractory classical Hodgkin lymphoma (cHL). To assess the complete metabolic response (CMR) rate and safety of pembrolizumab monotherapy in newly diagnosed cHL, we conducted a multicenter, single-arm, phase 2 investigator-initiated trial of sequential pembrolizumab and doxorubicin, vinblastine, and dacarbazine (AVD) chemotherapy. Patients ≥18 years of age with untreated, early, unfavorable, or advanced-stage disease were eligible for treatment. Thirty patients (early unfavorable stage, n = 12; advanced stage, n = 18) were treated with 3 cycles of pembrolizumab monotherapy followed by AVD for 4 to 6 cycles, depending on stage and bulk. Twelve had either large mediastinal masses or bulky disease (>10 cm). After pembrolizumab monotherapy, 11 patients (37%) demonstrated CMRs, and an additional 7 of 28 (25%) patients with quantifiable positron emission tomography computed tomography scans had >90% reduction in metabolic tumor volume. All patients achieved CMR after 2 cycles of AVD and maintained their responses at the end of treatment. With a median follow-up of 22.5 months (range, 14.2-30.6) there were no changes in therapy, progressions, or deaths. No patients received consolidation radiotherapy, including those with bulky disease. Therapy was well tolerated. The most common immune-related adverse events were grade 1 rash (n = 6) and grade 2 infusion reactions (n = 4). One patient had reversible grade 4 transaminitis and a second had reversible Bell’s palsy. Brief pembrolizumab monotherapy followed by AVD was both highly effective and safe in patients with newly diagnosed cHL, including those with bulky disease. This trial was registered at www.clinicaltrials.gov as #NCT03226249.

In a multicenter, phase 2, investigator-initiated trial of sequential pembrolizumab and AVD (doxorubicin, vinblastine, and dacarbazine), nearly two-thirds of patients with untreated, unfavorable, or advanced-stage classic Hodgkin lymphoma (cHL) achieved positron emission tomography (PET)–defined, complete or near-complete metabolic responses (CMRs), following pembrolizumab monotherapy. Furthermore, all patients achieved CMR after 2 cycles of AVD, with 100% of patients alive and without relapse at initial publication. We now report long-term follow-up, including the 3-year overall survival (OS) and planned correlative analyses. Thirty patients received 3 cycles of single-agent pembrolizumab, followed by AVD chemotherapy for 4 to 6 cycles depending on the stage and bulk. PET/computed tomography scan was performed after pembrolizumab monotherapy, 2 cycles of AVD, and at the end of therapy. Baseline biopsy samples were analyzed for genomic alterations of chromosome 9p24.1 and programmed cell death protein 1 (PD-1) pathway markers. At a median follow-up of 33.1 months (range, 26.0-43.0), progression-free survival and OS remained 100%. All patients had genomic alterations in 9p24.1 and were positive for programmed death ligand 1 (PD-L1) by immunohistochemistry. There was no relationship between depth of response to single-agent pembrolizumab and 9p24.1 alterations or PD-1 pathway H-scores. After additional follow-up, sequential pembrolizumab and AVD remained highly effective. The high response rates observed at all PD-L1 levels suggest that even low levels of PD-L1 expression are sufficient for response to PD-1 blockade in untreated cHL. An international phase 2 trial (registered at www.clinicaltrials.gov as #NCT03226249) is ongoing to confirm our findings.

PURPOSE: Variability in computed tomography images intrinsic to individual scanners limits the application of radiomics in clinical and research settings. The development of reproducible and generalizable radiomics-based models to assess lesions requires harmonization of data. The purpose of this study was to develop, test, and analyze the efficacy of a radiomics data harmonization model. MATERIALS AND METHODS: Radiomic features from biopsy-proven untreated hepatic metastasis (N = 380) acquired from 167 unique patients with pancreatic, colon, and breast cancers were analyzed. Radiomic features from volume-match 551 samples of normal liver tissue and 188 hepatic cysts were included as references. A novel linear mixed effect model was used to identify effects associated with lesion size, tissue type, and scanner model. Six separate machine learning models were then used to test the effectiveness of radiomic feature harmonization using multivariate analysis. RESULTS: Proposed model identifies and removes scanner-associated effects while preserving cancer-specific functional dependence of radiomic features on the tumor size. Data harmonization improves the performance of classification models by reducing the scanner-associated variability. For example, the multiclass logistic regression model, LogitBoost, demonstrated the improvement in sensitivity in the range from 15% to 40% for each type of liver metastasis, whereas the overall model accuracy and the kappa coefficient increased by 5% and 8% accordingly. CONCLUSION: The model removed scanner-associated effects while preserving cancer-specific functional dependence of radiomic features.

Background/Objectives: Radiomic features exhibit a correlation with tumor size on pretreatment images. However, on post-treatment images, this association is influenced by treatment efficacy and varies between responders and non-responders. This study introduces a novel model, called baseline-referenced Delta radiomics, which integrates the association between radiomic features and tumor size into Delta radiomics to predict chemotherapy response in liver metastases from breast cancer (BC) and colorectal cancer (CRC). Materials and Methods: A retrospective study analyzed contrast-enhanced computed tomography (CT) scans of 83 BC patients and 84 CRC patients. Among these, 57 BC patients with 106 liver lesions and 37 CRC patients with 109 lesions underwent post-treatment imaging after systemic chemotherapy. Radiomic features were extracted from up to three lesions per patient following manual segmentation. Tumor response was assessed by measuring the longest diameter and classified according to RECIST 1.1 criteria as progressive disease (PD), partial response (PR), or stable disease (SD). Classification models were developed to predict chemotherapy response using pretreatment data only, Delta radiomics, and baseline-referenced Delta radiomics. Model performance was evaluated using confusion matrix metrics. Results: Baseline-referenced Delta radiomics performed comparably or better than established radiomics models in predicting tumor response in chemotherapy-treated patients with liver metastases. The sensitivity, specificity, and balanced accuracy in predicting response ranged from 0.66 to 0.97, 0.81 to 0.97, and 80% to 90%, respectively. Conclusions: By integrating the relationship between radiomic features and tumor size into Delta radiomics, baseline-referenced Delta radiomics offers a promising approach for predicting chemotherapy response in liver metastases from breast and colorectal cancer.