Marco Ravanelli1, Giorgio Maria Agazzi2, Gianluca Milanese3, Elisa Roca4, Mario Silva5, Marcello Tiseo6, Paolo Rondi7, Alice Baggi8, Balaji Ganeshan9, Margherita Muri10, Stefano Panni11, Camilla Botti12, Nicola Sverzellati13, Roberto Maroldi14, Alfredo Berruti15, Davide Farina16. 1. University of Brescia, Department of Radiology, P.le Spedali Civili 1, 25123, Brescia, Italy. Electronic address: marcoravanelli@hotmail.it. 2. University of Brescia, Department of Radiology, P.le Spedali Civili 1, 25123, Brescia, Italy. Electronic address: giorgiomaria.agazzi@gmail.com. 3. University of Parma, Department of Radiology, via Università, 12, I 43121, Parma, Italy. Electronic address: gianluca.milanese@studenti.unipr.it. 4. University of Brescia, Department of Oncology, P.le Spedali Civili 1, 25123, Brescia, Italy. Electronic address: elisaroca@gmail.com. 5. University of Parma, Department of Radiology, via Università, 12, I 43121, Parma, Italy. Electronic address: mario.silva@unipr.it. 6. Department of Medicine and Surgery, University of Parma and Medical Oncology Unit, University Hospital of Parma, Italy. Electronic address: mtiseo@ao.pr.it. 7. University of Brescia, Department of Oncology, P.le Spedali Civili 1, 25123, Brescia, Italy. Electronic address: paolo.rondi92@gmail.com. 8. University of Brescia, Department of Oncology, P.le Spedali Civili 1, 25123, Brescia, Italy. Electronic address: a.baggi001@studenti.unibs.it. 9. University College London, Institute of Nuclear Medicine, Gower Street, London, United Kingdom. Electronic address: b.ganeshan@ucl.ac.uk. 10. ASST Cremona, Department of Radiology, Viale Concordia n 1, 26100, Cremona, Italy. Electronic address: margheritamuri@gmail.com. 11. ASST Cremona, Department of Oncology Italy. Electronic address: stefanopanni@alice.it. 12. University of Brescia, Department of Radiology, P.le Spedali Civili 1, 25123, Brescia, Italy. Electronic address: botti.camilla@gmail.com. 13. University of Parma, Department of Radiology, via Università, 12, I 43121, Parma, Italy. Electronic address: nicola.sverzellati@unipr.it. 14. University of Brescia, Department of Radiology, P.le Spedali Civili 1, 25123, Brescia, Italy. Electronic address: roberto.maroldi@unibs.it. 15. University of Brescia, Department of Oncology, P.le Spedali Civili 1, 25123, Brescia, Italy. Electronic address: alfredo.berruti@unibs.it. 16. University of Brescia, Department of Radiology, P.le Spedali Civili 1, 25123, Brescia, Italy. Electronic address: davide.farina@unibs.it.
Abstract
PURPOSE: The aim of this study was to assess computed-tomography histogram analysis (CTHA) as prognostic and predictive factor in platinum-refractory non-small cell lung carcinoma (NSCLC) treated with immune checkpoint inhibitor Nivolumab. METHOD: One hundred and four patients were enrolled from 3 different centers. CT was performed using similar parameters among different scanners. CTHA was performed with the proprietary software TexRAD, which extracts histogram features at different spatial scale (spatial scale filters, SSF) producing 30 CTHA features per patients. Cross-validated Least Absolute Shrinkage and Selection Operator LASSO was used to select those features which were related to overall and progression-free survival (OS and PFS, respectively). High- and low-risk subgroups were identified using the best cutoff. RESULTS: Median follow-up was 13.8 weeks. Median OS and PFS were 7.3 and 3 months, respectively. LASSO selected kurtosis obtained by SSF = 4 mm as the single feature related to OS, leading to an hazard ratio (HR) of 0.476 (95%CI 0.29-0.77). PFS was related with kurtosis SSF = 6 mm, with HR of 0.556 (95%CI 0.36-0.86). CONCLUSION: Despite its limitations, this study is the first which suggests that CTHA could play a role in stratifying prognosis and treatment response in patients with NSCLC treated with Nivolumab.
PURPOSE: The aim of this study was to assess computed-tomography histogram analysis (CTHA) as prognostic and predictive factor in platinum-refractory non-small cell lung carcinoma (NSCLC) treated with immune checkpoint inhibitor Nivolumab. METHOD: One hundred and four patients were enrolled from 3 different centers. CT was performed using similar parameters among different scanners. CTHA was performed with the proprietary software TexRAD, which extracts histogram features at different spatial scale (spatial scale filters, SSF) producing 30 CTHA features per patients. Cross-validated Least Absolute Shrinkage and Selection Operator LASSO was used to select those features which were related to overall and progression-free survival (OS and PFS, respectively). High- and low-risk subgroups were identified using the best cutoff. RESULTS: Median follow-up was 13.8 weeks. Median OS and PFS were 7.3 and 3 months, respectively. LASSO selected kurtosis obtained by SSF = 4 mm as the single feature related to OS, leading to an hazard ratio (HR) of 0.476 (95%CI 0.29-0.77). PFS was related with kurtosis SSF = 6 mm, with HR of 0.556 (95%CI 0.36-0.86). CONCLUSION: Despite its limitations, this study is the first which suggests that CTHA could play a role in stratifying prognosis and treatment response in patients with NSCLC treated with Nivolumab.
Authors: Balaji Ganeshan; Kenneth Miles; Asim Afaq; Shonit Punwani; Manuel Rodriguez; Simon Wan; Darren Walls; Luke Hoy; Saif Khan; Raymond Endozo; Robert Shortman; John Hoath; Aman Bhargava; Matthew Hanson; Daren Francis; Tan Arulampalam; Sanjay Dindyal; Shih-Hsin Chen; Tony Ng; Ashley Groves Journal: Cancers (Basel) Date: 2021-05-31 Impact factor: 6.639