Background: Whether radiotherapy only for primary lung tumor (RTPLT) after epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) therapy improves survival of treatment naïve advanced EGFR-mutant lung adenocarcinoma (LAD) patients with/without polymetastasis. Materials and Methods: This was a retrospective, single-center, observational study. Patients with stage IIIB-IV EGFR-mutant LAD with disease control by EGFR-TKI therapy were divided into curative RTPLT, and control, without radiotherapy (WRTPLT) groups. Results: A total of 138 patients were enrolled; 46 in the RTPLT group and 92 in the WRTPLT group. Amongst them, 37% had oligometastasis, and 26.1% brain metastasis. The RTPLT group had both significantly longer progression-free survival (PFS) (27.5 months [95% CI 18.1– 36.9] vs 10.9 months [95% CI 6.3– 15.5], P< 0.001) and overall survivor (OS) (NR [95% CI NR-NR] vs 38.0 months [95% CI 31.2– 44.8], P< 0.001), respectively, when compared to the WRTPLT group. In multivariate analysis, the adjusted HR of radiotherapy on PFS was 0.30 (0.19– 0.47) and on OS, 0.11 (0.04– 0.30). Patients with oligometastasis had significantly longer PFS than those with polymetastasis with an HR of 0.35 (0.14– 0.85), P=0.02. Patients with either oligometastasis or polymetastasis had significant longer PFS when undergoing radiotherapy than those without (both P< 0.05). An EGFR-TKI to radiotherapy interval < 24 weeks seemed more beneficial (P=0.097). Radiation pneumonitis comprised 32 (69.6%), 12 (26.1%), and two (4.3%) cases of common terminology criteria grade I, II, and III, respectively. Conclusion: Curative RTPLT can prolong survival in patients with LAD following EGFR-TKI disease control, both involving oligometastasis and polymetastasis. RTPLT within 24 weeks after EGFR-TKI initiation appeared to be more beneficial with tolerable radiation pneumonitis.

Background This study was designed to gauge the effectiveness of evaluation of tumor response and prognosis by positron emission tomography with 18F-fluorodeoxyglucose-positron emission tomography (FDG-PET) before and after preoperative chemoradiotherapy in patients with esophageal cancer. Methods Forty-nine patients from October 2008 to September 2012 with locally advanced stage esophageal carcinoma, clinical stage T2-4N0-3M0, who underwent preoperative chemoradiotherapy (preop CRT) followed by esophagectomy were enrolled in our study. All patients underwent two FDG-PET scans to compare those results with the pathologic results. Metabolic response of the primary tumor by the percentage change of the SUVmax/1 hour (ΔSUV) before and after preop CRT (ΔSUV was calculated as the difference between preop CRT SUVmax/1 hour and postop CRT SUVmax/1 hour divided by preop CRT SUVmax/1 hour at esophageal tumor) was evaluated for overall survival (OS), disease free survival (DFS), local recurrence rate, and distant failure free survival (DFFS). Prognostic factors such as age, different regimen of chemotherapy, pathologic stage, FDG-PET stage, endoscopic esophageal tumor length, and ΔSUV were analyzed. The number of highly suspect malignant lymph nodes was calculated by PET when SUVmax/1 hour ≥2.5 and by surgical removal. Sensitivity and specificity of regional lymph node detection by PET were also recorded. Results Upon univariate analysis, overall survival rate was related to ΔSUV >60% (p = 0.045), pathological N stage (p = 0.001), and endoscopic total length of esophageal tumor (p = 0.005). The result of FDG-PET scan after preop CRT had high specificity (96.7%) but low sensitivity (45.8%) in predicting the residual malignant lymph node numbers. The positive and the negative prediction rates were 44% and 96%, respectively. The result of the FDG-PET after preop CRT showed upstaged in 16 patients (32.6%), downstaged in nine patients (18.3%), and the same stage in 24 patients (48.9%) when compared with the pathologic stage. Conclusion The change of SUVmax can be a tool for evaluating tumor response after preop CRT. There is also a trend of good prognosis in overall survival rate when ΔSUV value is >60%.

Our study aimed to harness the power of CT scans, observed over time, in predicting how lung adenocarcinoma patients might respond to a treatment known as EGFR-TKI. Analyzing scans from 322 advanced stage lung cancer patients, we identified distinct image-based patterns. By integrating these patterns with comprehensive clinical information, such as gene mutations and treatment regimens, our predictive capabilities were significantly enhanced. Interestingly, the precision of these predictions, particularly related to radiomics features, diminished when data from various centers were combined, suggesting that the approach requires standardization across facilities. This novel method offers a potential pathway to anticipate disease progression in lung adenocarcinoma patients treated with EGFR-TKI, laying the groundwork for more personalized treatments. To further validate this approach, extensive studies involving a larger cohort are pivotal.

In the context of non-small cell lung cancer (NSCLC) patients treated with EGFR tyrosine kinase inhibitors (TKIs), this research evaluated the prognostic value of CT-based radiomics. A comprehensive systematic review and meta-analysis of studies up to April 2023, which included 3111 patients, was conducted. We utilized the Quality in Prognosis Studies (QUIPS) tool and radiomics quality scoring (RQS) system to assess the quality of the included studies. Our analysis revealed a pooled hazard ratio for progression-free survival of 2.80 (95% confidence interval: 1.87–4.19), suggesting that patients with certain radiomics features had a significantly higher risk of disease progression. Additionally, we calculated the pooled Harrell’s concordance index and area under the curve (AUC) values of 0.71 and 0.73, respectively, indicating good predictive performance of radiomics. Despite these promising results, further studies with consistent and robust protocols are needed to confirm the prognostic role of radiomics in NSCLC.

Background We investigated preoperative concurrent chemoradiotherapy (CCRT) with oxaliplatin for locally advanced, potentially operative esophageal cancer in this Phase II study. Methods Between October 2009 and October 2011, 35 consecutive patients with newly diagnosed esophageal cancer clinical stage T3-4, N0-1, M0 were enrolled into this study. One dose of chemotherapy with oxaliplatin (35 mg/m²) on Day 1 and Day 2, leucovorin (200 mg/m²) on Day 1, and 5-fluorouracil [5-FU; 2400 mg/m² intravenously (i.v.) administered continuously for 48 hours] on Day 1 was administered 2 weeks before preoperative CCRT. During preoperative CCRT, radiation dose of 4500 cGy in 25 fractions was administered to the clinical target volume and 5000 cGy to 5040 cGy in 25 fractions was administered to the gross tumor volume; chemotherapy is administered concomitantly with oxaliplatin (45 mg/m²) on Day 1 of radiation therapy (R/T) every 14 days; 5-FU (400 mg/m² i.v. bolus for 1 hour) for 5 days on Weeks 1 and 5 of R/T. Operation was performed 4–6 weeks after preoperative CCRT. Acute toxicity profile, overall survival rate, disease-free survival rate, distant metastasis failure-free survival rate, and local recurrence rate were evaluated. Results Four patients withdrew from the study. The total number of patients in this analysis was 31. The resection rate was 64.5%. The pathologic complete response rate was 15%. The overall median survival was 19.3 months. The 5-year overall survival rate was 37.8%. The 5-year disease-free survival rate was 31.1%. The 5-year distant metastasis failure-free survival rate was 40.7% (50.56% for patients with operation; 27.2% for patients without operation, p = 0.0298). The acute toxicities were mild, and no Grade 3 or above hematologic toxicity was noted. There was only one patient with Grade 3 esophagus toxicity. Grade 3 lung toxicity occurred in only three patients. Conclusion Preoperative chemoradiotherapy with oxaliplatin in the treatment of locally advanced, potentially resectable esophageal cancer is feasible and safe.

Low-dose computed tomography (LDCT) is the most effective tools for early detection of lung cancer. With advancements in artificial intelligence, various Computer-Aided Diagnosis (CAD) systems are now supported in clinical practice. For radiologists dealing with a huge volume of CT scans, CAD systems are helpful. However, the development of these systems depends on precisely annotated datasets, which are currently limited. Although several lung imaging datasets exist, there is only few of publicly available datasets with segmentation annotations on LDCT images. To address this problem, we developed a dataset based on NLST LDCT images with pixel-level annotations of lung lesions. The dataset includes LDCT scans from 605 patients and 715 annotated lesions, including 662 lung tumors and 53 lung nodules. Lesion volumes range from 0.03 cm³ to 372.21 cm³, with 500 lesions smaller than 5 cm³, mostly located in the right upper lung. A 2D U-Net model trained on the dataset achieved a 0.95 IoU on training dataset. This dataset enhances the diversity and usability of lung cancer annotation resources.