Adjuvant radiotherapy in node positive prostate cancer patients: a debate still on. when, for whom?

OBJECTIVE: To evaluate the impact of adjuvant radiotherapy (aRT) in patients with prostate cancer (PCa) found to have pathological positive lymph nodes (pN1s) after radical prostatectomy (RP) and extended pelvic lymph node dissection (ePLND) with regard to distant recurrence-free survival (RFS), according to both main tumour pathological characteristics and number of positive lymph nodes. Biochemical RFS, local RFS, overall survival (OS) and acute and late toxicity were assessed as secondary endpoints. PATIENTS AND METHODS: A retrospective cohort of 187 consecutive patients with pN1 PCa were treated with aRT at the IEO, European Institute of Oncology IRCCS, Milan, Italy. aRT on the tumour bed and pelvis was administered within 6 months of RP. Androgen deprivation therapy was administered according to the guidelines. Univariate and multivariate Cox regression analyses predicting biochemical RFS, local RFS, distant RFS and OS rates were performed to assess whether the number of pN1s represented an independent prognostic factor. The Youden index was computed to find the optimal threshold for the number of pN1s able to discriminate between patients with or without biochemical and clinical relapse. RESULTS: At 5 years, local RFS, distant RFS, biochemical RFS and OS were 68%, 71%, 56% and 94%, respectively. The median follow-up was 49 months. The number of pN1s was significantly associated with biochemical RFS, local RFS and distant RFS. The best threshold for discriminating between patients with or without biochemical and clinical relapse was five pN1s. In multivariate analyses, the number of pN1s was confirmed to be an independent predictor of biochemical RFS, local RFS and distant RFS, but not of OS. Multivariate analyses also showed an increased risk of biochemical relapse for increasing values of initial prostate-specific antigen and for patients with tumour vascular invasion. Local and distant RFS were also inversely correlated with significantly reduced risk for International Society of Urological Pathology grade group <3 (group 1 or 2 compared to group 3). CONCLUSIONS: Our data confirmed the encouraging outcomes of patients with pN1 PCa treated with adjuvant treatments and the key role represented by the number of pN1s in predicting biochemical RFS, clinical RFS and distant RFS. Large prospective cohort studies and randomized clinical trials are needed to confirm these results and to identify the subgroup of patients with pN1 PCa who would most benefit from aRT.

Preliminary report on harmonization of features extraction process using the ComBat tool in the multi-center "Blue Sky Radiomics" study on stage III unresectable NSCLC.

BACKGROUND AND PURPOSE: In the retrospective-prospective multi-center "Blue Sky Radiomics" study (NCT04364776), we plan to test a pre-defined radiomic signature in a series of stage III unresectable NSCLC patients undergoing chemoradiotherapy and maintenance immunotherapy. As a necessary preliminary step, we explore the influence of different image-acquisition parameters on radiomic features' reproducibility and apply methods for harmonization. MATERIAL AND METHODS: We identified the primary lung tumor on two computed tomography (CT) series for each patient, acquired before and after chemoradiation with i.v. contrast medium and with different scanners. Tumor segmentation was performed by two oncological imaging specialists (thoracic radiologist and radio-oncologist) using the Oncentra Masterplan® software. We extracted 42 radiomic features from the specific ROIs (LIFEx). To assess the impact of different acquisition parameters on features extraction, we used the Combat tool with nonparametric adjustment and the longitudinal version (LongComBat). RESULTS: We defined 14 CT acquisition protocols for the harmonization process. Before harmonization, 76% of the features were significantly influenced by these protocols. After, all extracted features resulted in being independent of the acquisition parameters. In contrast, 5% of the LongComBat harmonized features still depended on acquisition protocols. CONCLUSIONS: We reduced the impact of different CT acquisition protocols on radiomic features extraction in a group of patients enrolled in a radiomic study on stage III NSCLC. The harmonization process appears essential for the quality of radiomic data and for their reproducibility. ClinicalTrials.gov Identifier: NCT04364776, First Posted:April 28, 2020, Actual Study Start Date: April 15, 2020, https://clinicaltrials.gov/ct2/show/NCT04364776 .

Radiomics features as predictive and prognostic biomarkers in NSCLC.

Introduction: Radiomics extracts a large amount of quantitative information from medical images using specific data characterization algorithms. This information, called radiomic features, can be combined with clinical data to build prediction models for prognostic evaluation and treatment selection.Areas covered: We outlined a series of studies investigating the correlation between radiomics features and outcome (prognostic) as well as response to therapy (predictive) in non-small cell lung cancer (NSCLC). We performed our analysis both in the setting of early and advanced stage of disease, with a focus on the different therapies and imaging modalities adopted.Expert opinion: The prognostic and predictive potential of the radiomic approach, combined with clinical models, could help decision-making process and guide toward the creation of an optimal and 'tailored' therapeutic strategy for lung cancer patients. However, due to the low reproducibility of most of the conducted studies and the lack of validated results, such a desirable scenario has not yet been translated to routine clinical practice.