Evaluation of the efficacy of pretreatment chest CT markers in predicting response to neoadjuvant chemoradiotherapy in locally advanced non-small cell lung cancer (NSCLC)

Year 2024, , 32 - 41, 11.03.2024

Hüseyin Akkaya , Okan Dılek , Rukiye Aysu Revanlı Saygılı Ahmet Gulmez , Hatice Coşkun , Zeynel Abidin Taş , Bozkurt Gülek

https://doi.org/10.36516/jocass.1427896

Abstract

Aim: To investigate baseline enhanced chest CT findings that may predict progression or response to neoadjuvant chemoradiotherapy.
Materials and methods: Multiple parameters to be obtained from baseline enhanced chest CT scans of 140 patients with NSCLC who had baseline enhanced chest CT scans before neoadjuvant chemoradiotherapy were noted. In addition to CT features of tumour tissues, age, gender, tumour cell types, lymph node TNM stages, distant metastases on baseline enhanced chest CT, bronchial and vascular invasion were also evaluated. Chest CT findings and changes in tumour tissue at 3 and 6 months during neoadjuvant treatment were noted. Patients were operated after the end of neoadjuvant treatment. It was investigated which parameters could predict response to neoadjuvant treatment and which findings could predict progression.
Results: Progression and mortality rates were found to be low in patients with remission (p<0.001). None of the parameters on baseline chest CT before neoadjuvant treatment predicted response to neoadjuvant treatment. According to the results of the analysis, patients with lymph node station had a 3.69 -fold efect [odds ratio (OR)=3.693, [95% confdence interval (CI)= 1.875–7.274, p=0.041] effect on progression (p<0.001).

Conclusion: It has been observed that any of the parameters that can be obtained from baseline chest CT examination before neoadjuvant treatment are not successful in predicting neoadjuvant treatment response. Lymph node is the only baseline chest CT finding that can predict progression.

Keywords

neoadjuvant chemoradiotherapy, non-small cell lung cancer, chest CT, prognosis, pathologic response

Ethical Statement

Ethics committee and Turkish Ministry of Health approvals were obtained for the study (2023/2767).

Lokal ileri küçük hücreli dışı akciğer kanserinde (KHDAK) neoadjuvan kemoradyoterapiye yanıtı öngörmede tedavi öncesi toraks BT belirteçlerinin etkinliğinin değerlendirilmesi

Abstract

Amaç: Neoadjuvan kemoradyoterapiye yanıtı veya progresyonu öngörebilecek tedavi öncesi toraks BT bulgularını araştırmak.
Gereçler ve yöntemler: Neoadjuvan kemoradyoterapi öncesinde başlangıçta kontrastlı toraks BT taramaları yapılan 140 küçük hücreli dışı akciğer kanseri hastasının bazal toraks BT taramalarından elde edilecek çok sayıda parametre kaydedildi.
Tümör dokularının BT özelliklerine ek olarak yaş, cinsiyet, tümör hücre tipleri, lenf nodu, TNM evreleri, bazal toraks BT'de uzak metastazlar, bronşiyal ve vasküler invazyon da değerlendirildi. Neoadjuvan tedavi sırasında 3. ve 6. ayda toraks BT bulguları ve tümör dokusundaki değişiklikler kaydedildi. Neoadjuvan tedavinin bitiminden sonra hastalar ameliyata alındı. Neoadjuvan tedaviye yanıtı hangi parametrelerin öngörebileceği, hangi bulguların progresyonu öngörebileceği araştırıldı.

Bulgular: Remisyona giren hastalarda progresyon ve mortalite oranlarının düşük olduğu görüldü (p<0,001). Neoadjuvan tedavi öncesi başlangıçtaki göğüs BT'sindeki parametrelerin hiçbiri neoadjuvan tedaviye yanıtı öngörmedi. Analiz sonuçlarına göre lenf nodu istasyonu olan hastaların progresyona etkisi 3,69 kat [olasılık oranı (OR)=3,693, [%95 güven aralığı (CI)= 1,875–7,274, p=0,041] idi (p <0,001).

Sonuç: Neoadjuvan tedavi öncesi bazal göğüs BT incelemesinden elde edilebilecek parametrelerden herhangi birinin neoadjuvan tedavi yanıtını öngörmede başarılı olmadığı görülmüştür. Lenf nodu, progresyonu tahmin edebilen tek bazal toraks BT bulgusudur.

Keywords

neoadjuvan kemoradyoterapi, küçük hücreli dışı akciğer kanseri, toraks BT, prognoz, patolojik yanıt

References

• 1. Siegel RL, Miller KD, Fuchs HE, Jemal A Cancer statistics, 2021. CA Cancer J Clin. 2021;71:7-33.
• 2. Wang Q, Wang S, Sun Z, Cao M, Zhao X. Evaluation of log odds of positive lymph nodes in predicting the survival of patients with non-small cell lung cancer treated with neoadjuvant therapy and surgery: a SEER cohort-based study. BMC Cancer. 2022;22:801.
• 3. Shen J, Sun N, Zens P et al. Spatial metabolomics for evaluating response to neoadjuvant therapy in non-small cell lung cancer patients. Cancer Commun (Lond). 2022;42:517-535.
• 4. Saw SPL, Ong BH, Chua KLM et al. Revisiting neoadjuvant therapy in non-small-cell lung cancer. Lancet Oncol. 2021;22:e501-e516.
• 5. Rosner S, Liu C, Forde PM et al. Association of Pathologic Complete Response and Long-Term Survival Outcomes Among Patients Treated With Neoadjuvant Chemotherapy or Chemoradiotherapy for NSCLC: A Meta-Analysis. JTO Clin Res Rep. 2022 Jul 31;3(9):100384.
• 6. Godoy LA, Chen J, Ma W P et al. Emerging precision neoadjuvant systemic therapy for patients with resectable non-small cell lung cancer: current status and perspectives. Biomark Res 2023;11:7.
• 7. Blumenthal GM, Bunn PA Jr, Chaft JE et al. Current Status and Future Perspectives on Neoadjuvant Therapy in Lung Cancer. J Thorac Oncol 2018;13:1818-31.
• 8. Liang J, Bi N, Wu S et al. Etoposide and cisplatin versus paclitaxel and carboplatin with concurrent thoracic radiotherapy in unresectable stae III non-small cell lung cancer: a multicenter randomized phase III trial. Ann Oncol. 2017;28):777-783.
• 9. Cheng Y, Chen ZY, Huang JJ et al. Efficacy evaluation of neoadjuvant immunotherapy plus chemotherapy for non-small-cell lung cancer: comparison of PET/CT with postoperative pathology. Eur Radiol. 2023;33:6625-6635.
• 10. Khorrami M, Jain P, Bera K et al. Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features. Lung Cancer. 2019;135:1-9.
• 11. Yue D, Liu W, Chen C et al. Circulating tumor DNA predicts neoadjuvant immunotherapy efficacy and recurrence-free survival in surgical non-small cell lung cancer patients. Transl Lung Cancer Res. 2022;11:263-276.
• 12. Bao Y, Gu C, Xie H et al. Comprehensive study of neoadjuvant targeted therapy for resectable non-small cell lung cancer. Ann Transl Med. 2021;9:493.
• 13. Tanahashi M, Suzuki E, Yoshii N et al. Role of fluorodeoxyglucose-positron emission tomography in predicting the pathological response and prognosis after neoadjuvant chemoradiotherapy for locally advanced non-small-cell lung cancer. Interact Cardiovasc Thorac Surg. 2022;35:113.
• 14. Zhang J, Zhao X, Zhao Y et al. Value of pre-therapy 18F-FDG PET/CT radiomics in predicting EGFR mutation status in patients with non-small cell lung cancer. Eur J Nucl Med Mol Imaging. 2020;47:1137-1146.
• 15. Chetan MR, Gleeson FV. Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives. Eur Radiol. 2021;31:1049-1058.
• 16. Dissaux G, Visvikis D, Da-Ano R et al. Pretreatment 18F-FDG PET/CT Radiomics Predict Local Recurrence in Patients Treated with Stereotactic Body Radiotherapy for Early-Stage Non-Small Cell Lung Cancer: A Multicentric Study. J Nucl Med. 2020;61:814-820.
• 17. Nestle U, Schimek-Jasch T, Kremp S et al. Imaging-based target volume reduction in chemoradiotherapy for locally advanced non-small-cell lung cancer (PET-Plan): a multicentre, open-label, randomized, controlled trial. Lancet Oncol. 2020;21:581-592.
• 18. Khorrami M, Prasanna P, Gupta A, Patil P et al. Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in Non-Small Cell Lung Cancer. Cancer Immunol Res. 2020;8:108-119.
• 19. Bortolotto C, Lancia A, Stelitano C et al. Radiomics features as predictive and prognostic biomarkers in NSCLC. Expert Rev Anticancer Ther. 2021;21:257-266.
• 20. Liberini V, Mariniello A, Righi L et al. NSCLC Biomarkers to Predict Response to Immunotherapy with Checkpoint Inhibitors (ICI): From the Cells to In Vivo Images. Cancers (Basel). 2021;13:4543.
• 21. Seban RD, Mezquita L, Berenbaum A et al. Baseline metabolic tumor burden on FDG PET/CT scans predicts outcome in advanced NSCLC patients treated with immune checkpoint inhibitors. Eur J Nucl Med Mol Imaging. 2020;47:1147-1157.
• 22. Rosner S, Liu C, Forde PM et al. Association of Pathologic Complete Response and Long-Term Survival Outcomes Among Patients Treated With Neoadjuvant Chemotherapy or Chemoradiotherapy for NSCLC: A Meta-Analysis. JTO Clin Res Rep. 2022;3:100384.
• 23. Zarogoulidis P, Matthaios D, Kosmidis C et al. Effective early diagnosis for NSCLC: an algorithm. Expert Rev Respir Med. 2021;15:1437-1445.
• 24. Han Y, Ma Y, Wu Z et al. Histologic subtype classification of non-small cell lung cancer using PET/CT images. Eur J Nucl Med Mol Imaging. 2021;48:350-360.
• 25. Koyasu S, Nishio M, Isoda H et al. Usefulness of gradient tree boosting for predicting histological subtype and EGFR mutation status of non-small cell lung cancer on 18F FDG-PET/CT. Ann Nucl Med. 2020;34:49-57.
• 26. Dissaux G, Visvikis D, Da-Ano R et al. Pretreatment 18F-FDG PET/CT Radiomics Predict Local Recurrence in Patients Treated with Stereotactic Body Radiotherapy for Early-Stage Non-Small Cell Lung Cancer: A Multicentric Study. J Nucl Med. 2020;61:814-820.
• 27. Leader AM, Grout JA, Maier BB et al. Single-cell analysis of human non-small cell lung cancer lesions refines tumor classification and patient stratification. Cancer Cell. 2021;39:1594-1609.
• 28. Kan CFK, Unis GD, Li LZ et al. Circulating Biomarkers for Early Stage Non-Small Cell Lung Carcinoma Detection: Supplementation to Low-Dose Computed Tomography. Front Oncol. 2021;11:555331.
• 29. Hattori A, Suzuki K, Takamochi K et al. Japan Clinical Oncology Group Lung Cancer Surgical Study Group. Prognostic impact of a ground-glass opacity component in clinical stage IA non-small cell lung cancer. J Thorac Cardiovasc Surg. 2021;161:1469-1480.
• 30. Akinci D'Antonoli T, Farchione A, Lenkowicz J et al. CT Radiomics Signature of Tumor and Peritumoral Lung Parenchyma to Predict Nonsmall Cell Lung Cancer Postsurgical Recurrence Risk. Acad Radiol. 2020;27:497-507.
• 31. Nakanishi Y, Masuda T, Yamaguchi K et al. Pre-existing interstitial lung abnormalities are risk factors for immune checkpoint inhibitor-induced interstitial lung disease in non-small cell lung cancer. Respir Investig. 2019;57:451-459.