Prognostic value of genetic alterations and 18F-FDG PET/CT imaging features in diffuse large B cell lymphoma

• Published in: American journal of cancer research Vol. 13; no. 2; pp. 509 - 525
• Main Authors: Ferrer-Lores, Blanca, Lozano, Jose, Fuster-Matanzo, Almudena, Mayorga-Ruiz, Irene, Moreno-Ruiz, Paula, Bellvís, Fuensanta, Teruel, Ana B, Saus, Ana, Ortiz, Alfonso, Villamón-Ribate, Eva, Serrano-Alcalá, Alicia, Piñana, José L, Sopena, Pablo, Dosdá, Rosa, Solano, Carlos, Alberich-Bayarri, Ángel, Terol, María José
• Format: Journal Article
• Language: English
• Published: e-Century Publishing Corporation 01.01.2023
• Subjects: Original
• ISSN: 2156-6976; 2156-6976
• DOI: 10.2156/6976/ajcr0146852

The current standard front-line therapy for patients with diffuse large-B cell lymphoma (DLBCL)-rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP)-is found to be ineffective in up to one-third of them. Thus, their early identification is an important step towards testing alternative treatment options. In this retrospective study, we assessed the ability of 18F-FDG PET/CT imaging features (radiomic + PET conventional parameters) plus clinical data, alone or in combination with genomic parameters to predict complete response to first-line treatment. Imaging features were extracted from images prior treatment. Lesions were segmented as a whole to reflect tumor burden. Multivariate logistic regression predictive models for response to first-line treatment trained with clinical and imaging features, or with clinical, imaging, and genomic features were developed. For imaging feature selection, a manual selection approach or a linear discriminant analysis (LDA) for dimensionality reduction were applied. Confusion matrices and performance metrics were obtained to assess model performance. Thirty-three patients (median [range] age, 58 [49-69] years) were included, of whom 23 (69.69%) achieved long-term complete response. Overall, the inclusion of genomic features improved prediction ability. The best performance metrics were obtained with the combined model including genomic data and built applying the LDA method (AUC of 0.904, and 90% of balanced accuracy). The amplification of BCL6 was found to significantly contribute to explain response to first-line treatment in both manual and LDA models. Among imaging features, radiomic features reflecting lesion distribution heterogeneity (GLSZM_GrayLevelVariance, Sphericity and GLCM_Correlation) were predictors of response in manual models. Interestingly, when the dimensionality reduction was applied, the whole set of imaging features-mostly composed of radiomic features-significantly contributed to explain response to front-line therapy. A nomogram predictive for response to first-line treatment was constructed. In summary, a combination of imaging features, clinical variables and genomic data was able to successfully predict complete response to first-line treatment in DLBCL patients, with the amplification of BCL6 as the genetic marker retaining the highest predictive value. Additionally, a panel of imaging features may provide important information when predicting treatment response, with lesion dissemination-related radiomic features deserving especial attention.The current standard front-line therapy for patients with diffuse large-B cell lymphoma (DLBCL)-rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP)-is found to be ineffective in up to one-third of them. Thus, their early identification is an important step towards testing alternative treatment options. In this retrospective study, we assessed the ability of 18F-FDG PET/CT imaging features (radiomic + PET conventional parameters) plus clinical data, alone or in combination with genomic parameters to predict complete response to first-line treatment. Imaging features were extracted from images prior treatment. Lesions were segmented as a whole to reflect tumor burden. Multivariate logistic regression predictive models for response to first-line treatment trained with clinical and imaging features, or with clinical, imaging, and genomic features were developed. For imaging feature selection, a manual selection approach or a linear discriminant analysis (LDA) for dimensionality reduction were applied. Confusion matrices and performance metrics were obtained to assess model performance. Thirty-three patients (median [range] age, 58 [49-69] years) were included, of whom 23 (69.69%) achieved long-term complete response. Overall, the inclusion of genomic features improved prediction ability. The best performance metrics were obtained with the combined model including genomic data and built applying the LDA method (AUC of 0.904, and 90% of balanced accuracy). The amplification of BCL6 was found to significantly contribute to explain response to first-line treatment in both manual and LDA models. Among imaging features, radiomic features reflecting lesion distribution heterogeneity (GLSZM_GrayLevelVariance, Sphericity and GLCM_Correlation) were predictors of response in manual models. Interestingly, when the dimensionality reduction was applied, the whole set of imaging features-mostly composed of radiomic features-significantly contributed to explain response to front-line therapy. A nomogram predictive for response to first-line treatment was constructed. In summary, a combination of imaging features, clinical variables and genomic data was able to successfully predict complete response to first-line treatment in DLBCL patients, with the amplification of BCL6 as the genetic marker retaining the highest predictive value. Additionally, a panel of imaging features may provide important information when predicting treatment response, with lesion dissemination-related radiomic features deserving especial attention.