In this study we constructed radiomics nomogram with 9 CT-based radiomics features linked to OS in patients with SCLC and assessed its additional value over clinical risk factors for personalized OS estimation. Furthermore, we investigated the association between the RS and PCI, and stratified patients based on their RS to help identifying those who may benefit from PCI.

Radiomics, a field advancing swiftly, shows significant potential. It illustrates intra-tumor heterogeneity and pathophysiological insights by extracting extensive quantitative features from images and constructing models through a variety of statistical techniques [19]. Radiomics provides relevant data for the identification and grading of tumors, the assessment of treatment efficacy and prognosis, and the customization of individualized treatment strategies [20, 21]. The prognosis for patients with SCLC shows considerable variability, even when they find themselves at identical stages and are subjected to uniform therapeutic regimens. This highlights the need for accurate prognosis evaluation to facilitate the execution of individualized treatment strategies. While earlier research has demonstrated the significance of radiomics features in prognostic prediction of non-SCLC [13, 14, 22], its potential in predicting the survival outcomes for SCLC patients has remained relatively unexplored. Jain et al. [23] indicated that radiomics features obtained from within and around tumors hold the capability to predict OS of SCLC patients. Conversely, in a separate investigation, Gkika et al. [24] communicated that they failed to identify any radiomics features significantly linked to the OS of SCLC patients. In view of the small sample size of the above two studies, only 153 and 98 cases respectively, the results lack reliability.

Our analysis presented additional evidence from a multicenter study involving 375 patients with SCLC that CT-based radiomics features could independently predict OS. The utilization of 9 radiomics features for constructing the RS effectively segregated these patients into low and high RS groups, with Kaplan–Meier curves clearly showed significant differences in OS between different risk stratified groups. This methodology could potentially enable clinicians to choose personalized treatments of heightened efficacy for patients facing varying degrees of risk. Moreover, upon the stratification of patients according to their clinical stage, significant differences in OS between the low RS and high RS groups within both the limited and extensive stages. These findings imply a notable diversity in survival outcomes among patients categorized by RS, even when they share the same clinical stage. This finding is consistent with a study by Wang et al. [14], which demonstrated in non-SCLC that subgroup analysis by clinical and pathological stage revealed patients with high RS had significantly shorter recurrence-free survival than those with low RS, even within the same stage.

A nomogram is constructed using multi-factor regression analysis, incorporating various predictive markers to estimate specific clinical outcomes or event probabilities. It demonstrates notable precision in prognosticating certain malignant tumor outcomes [25, 26]. Our investigation demonstrates that the radiomics nomogram outperforms the clinical nomogram in predictive efficacy, as evidenced by measures such as C-index, AUC, calibration curve, DCA curve, NRI, and IDI. Nevertheless, this study yielded that the clinical-radiomics nomogram held no notable superiority over the radiomics nomogram. This observation further corroborated the limitations of clinical variables, accentuating the distinct benefit of the radiomics nomogram when it comes to prognosticating OS in SCLC. This could potentially result from the limitation of clinical factors in reflecting tumor heterogeneity, unable to provide a comprehensive reflection of the overall tumor metabolism. Radiomics based on CT images can provide a comprehensive and quantitative depiction of tumors’ biological characteristics. Moreover, it adeptly elucidates the nuanced aspects present within images, thereby facilitating an exploration of potential pathophysiological information [27]. This capability might elucidate the improved efficacy in prognostication. A previous large-scale study established a clinical OS prognostic nomogram based on 24,680 patients with SCLC, and validated in an independent testing group (9,700 SCLC patients) with a C-index of 0.722 and an integrated AUC of 0.79 [28]. Given the large variation in sample size, the clinical nomogram of our study (C-index: 0.625, 0.570, AUC: 0.576–0.692 in validation cohorts) is significantly inferior to the above study, but the radiomics nomogram (C-index: 0.770, 0.763, AUC: 0.766–0.893 in validation cohorts) seems to be superior.

Brain metastases are one of the most common sites of metastasis in patients with SCLC, with approximately 10% of patients developing brain metastases at first diagnosis [29]. As a preventive measure for SCLC patients at risk of brain metastases, whole-brain radiotherapy, known as PCI, is employed. A clinical study conducted by the European Organization for Research and Treatment of Cancer (EORTC) demonstrated that PCI enhances OS in patients with extensive stage SCLC [30]. Consequently, international guidelines recommend PCI for SCLC patients who have achieved complete or partial responses after chemoradiotherapy. Nevertheless, the incorporation of brain MRI in clinical practice, multiple studies have suggested that PCI might not be advantageous for all individuals [6, 7]. For instance, Pezzi et al. [6] reported that PCI did not correlate with improved OS in limited-stage SCLC (P = 0.32). Additionally, a phase 3 trial conducted involving 47 institutions in Japan found that PCI did not result in an extended OS for patients with extensive stage SCLC (P = 0.094) [7].

To further evaluate the benefits of PCI, our study was the first to introduce a risk stratification method using RS to identify SCLC patients who could potentially benefit from PCI. The most significant finding of our study was that patients with high RS, experienced significant benefits from PCI, whether in the limited or extensive stage (P = 0.003, 0.019, respectively). In contrast, those with low RS did not exhibit any clear benefit from PCI (P = 0.085, 0.986, respectively). These results suggest that, according to the RS-based stratification, PCI may be unnecessary for patients with a low RS. Prior researches have established that PCI is linked to significant neurotoxicity, leading to impairments in brain structure and cognitive function, which in turn impacts patients’ quality of life [8, 31]. This may explain why patients with low RS do not experience benefits from PCI. Additionally, a meta-analysis revealed that stage pT1–2N0M0 patients had a risk of brain metastases was just 12% even in the absence of PCI [32]. As a result, patients with low RS are less likely to develop brain metastases. In contrast, those with high RS face a worse prognosis, and PCI may provide survival benefits for them.

This study has several limitations that should be noted. Firstly, owing to its retrospective nature, potential biases in the selection of the sample size might have occurred, and the verification of clinical application lacked multi-center prospective studies. Future studies will be conducted to analyze the survival benefits of different stage for SCLC based on radiomics, so as to achieve accurate personalized treatment and optimize treatment plans. Secondly, the intricacy, time consumption, and labor intensiveness associated with manual segmentation are noteworthy. Consequently, the adoption of more dependable and time-efficient alternatives, such as automated segmentation should be contemplated [33]. Lastly, the CT image slice thickness measured 5 mm, a factor that might compromise the precision of feature extraction in comparison to the thin-slice CT.

In summary, a CT-based radiomics nomogram serves as an effective tool for predicting OS in patients with SCLC, offering additional value to clinical risk factors in estimating individual OS. Furthermore, risk stratification of RS could assist in determining which patients are more likely to benefit from PCI.