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Background: Neoadjuvant chemoradiotherapy (nCRT) has improved the resectability and survival of operable oesophageal squamous cell carcinoma (OSCC). We aimed to study if nCRT for OSCC makes minimally invasive oesophagectomy (MIO) technically more challenging and if the peri-operative and oncological outcomes are acceptable for MIO following nCRT. Materials and Methods: A retrospective review of patients with OSCC (cT1-2N1-2, cT3-4aN0-2) treated with nCRT and MIO between 2013 and 2019 was performed. The operative details including the technical difficulty in tumour dissection and lymphadenectomy, the post-operative complications and oncological outcomes were studied. Results: Seventy-five patients (male:female - 50:25; mean [range] age - 55.49 ± 8.43 [22–72] years; stage II - 34.7%; stage III - 37.3%; stage IVA - 28.0%) were enrolled. The concurrent chemotherapy course was completed by 25.3% of patients and the most common reason limiting the completion of chemotherapy was neutropaenia (66.0%). A thoraco-laparoscopic (n = 60) or hybrid (n = 15) McKeown’s oesophagectomy with a two-field lymphadenectomy was performed. The increased surgical difficulty was reported in 41 (54.7%) patients, particularly for mid-thoracic tumours and tumours exhibiting incomplete response. The 30-day overall and major complication rate was 48.0% and 20.0%, respectively, and there was no mortality. The rate of R0 resection, pathological complete response and median lymph nodal yield were 93.3%, 48% and 8 (range: 1–25), respectively. The mean overall survival (OS) was 62.2 months (95% confidence interval [CI]: 52.6–71.8) and recurrence-free survival (RFS) was 53.5 months (95% CI: 43.5–63.5). The 1-, 2- and 3-year OS and RFS were 89.5%, 78.8% and 64.4% and 71.1%, 61.3% and 56.6%, respectively. Conclusion: Minimally invasive McKeown’s oesophagectomy is feasible and safe in patients with OSCC receiving nCRT. The radiation component of nCRT increases the degree of operative difficulty, especially in relation to the supracarinal dissection and lymphadenectomy. However, this drawback did not adversely affect the short-term surgical or the long-term oncological outcomes. [ABSTRACT FROM AUTHOR]

Radiomics involves the extraction of information from medical images that are not visible to the human eye. There is evidence that these features can be used for treatment stratification and outcome prediction. However, there is much discussion about the reproducibility of results between different studies. This paper studies the reproducibility of CT texture features used in radiomics, comparing two feature extraction implementations, namely the MATLAB toolkit and Pyradiomics, when applied to independent datasets of CT scans of patients: (i) the open access RIDER dataset containing a set of repeat CT scans taken 15 min apart for 31 patients (RIDER Scan 1 and Scan 2, respectively) treated for lung cancer; and (ii) the open access HN1 dataset containing 137 patients treated for head and neck cancer. Gross tumor volume (GTV), manually outlined by an experienced observer available on both datasets, was used. The 43 common radiomics features available in MATLAB and Pyradiomics were calculated using two intensity-level quantization methods with and without an intensity threshold. Cases were ranked for each feature for all combinations of quantization parameters, and the Spearman's rank coefficient, rs , calculated. Reproducibility was defined when a highly correlated feature in the RIDER dataset also correlated highly in the HN1 dataset, and vice versa. A total of 29 out of the 43 reported stable features were found to be highly reproducible between MATLAB and Pyradiomics implementations, having a consistently high correlation in rank ordering for RIDER Scan 1 and RIDER Scan 2 ( rs > 0.8). 18/43 reported features were common in the RIDER and HN1 datasets, suggesting they may be agnostic to disease site. Useful radiomics features should be selected based on reproducibility. This study identified a set of features that meet this requirement and validated the methodology for evaluating reproducibility between datasets., Competing Interests: Conflicts of Interest: The authors have no relevant conflicts of interest to disclose.