A novel histopathological feature of spatial tumor–stroma distribution predicts lung squamous cell carcinoma prognosis
INTRODUCTION Squamous cell carcinoma is one of the major histological types of lung cancer, along with adenocarcinoma, and accounts for 15%–20% of lung cancer cases. Although remarkable progress has been made in lung cancer treatment in recent years, , the prognosis of squamous cell carcinoma after resection remains poor. The recurrence rate of squamous cell carcinoma after surgical resection ranges from 25% to 40%. , One of the most important issues in the pathological examination of lung squamous cell carcinoma is that there are few pathological indicators that can estimate the tumor malignancy and patient outcomes. , For adenocarcinoma, prognostic markers have been described in a number of articles. , , In particular, the histological grading system based on tissue architecture has been widely acknowledged as a reliable predictor of patient prognosis and is utilized in practical pathological diagnoses. , In contrast, histological grading of squamous cell carcinoma based on the degree of keratinization does not predict patient outcomes. Several histological findings have been proposed as potential predictors. Tumor budding has been reported to be associated with a poor prognosis. , Moreover, one of our studies showed that an alveolar space‐filling non‐destructive growth pattern predicts a good prognosis in squamous cell carcinoma arising in the periphery of the lungs. However, none of these has been widely accepted as predictors of patient outcomes to date, other than conventional histological findings, including lymphovascular invasion, visceral pleural invasion, and pathological stage. , Cancer tissues are a complex mixture of cancer and stromal cells, with the stroma providing a microenvironment that supports tumor growth and resistance to treatment. , Our group has shown that stromal cells, including cancer‐associated fibroblasts (CAFs) and tumor‐associated macrophages (TAMs), play significant roles in cancer cells. , , Moreover, we demonstrated that the amount of cancer stroma in cancer tissues indicated poor patient outcomes after the resection of peripheral lung squamous cell carcinoma. Considering the relationship between cancer cells and the surrounding stroma, their spatial distribution in cancer tissues may be crucial; however, previous studies have focused solely on their quantity, with little attention given to spatial distribution. , Therefore, this study aimed to analyze the clinical relevance of the spatial distribution of cancer cells and stroma in cancer tissues. To characterize the spatial distribution of the cancer elements, we utilized texture features, referring to previous analyses in the radiology field that focused on the clinical relevance of texture features, including Shannon's entropy in lung cancer images. , , In this study, we employed a cutting‐edge approach by incorporating machine learning‐based image processing and analysis with texture features, and investigated its clinical impact in lung squamous cell carcinoma patients.   MATERIALS AND METHODS 2.1 Patient selection The patient cohort and tissue specimens have been described previously. In short, 132 patients with peripheral lung squamous cell carcinoma measuring 3–5 cm who received surgical resection at the National Cancer Center Hospital East between 2002 and 2015 were enrolled. Cases that received neoadjuvant therapy were not included in this study. 2.2 Histological diagnosis Disease stages were classified according to the 8th edition of the TNM classification of malignant tumors. Histological diagnosis was made following the 5th edition of the World Health Organization series on histological classification. 2.3 Clinicopathological characteristics The clinicopathological characteristics of the patients were collected from the available medical records. The following factors were retrospectively analyzed: sex, age, smoking history, clinical stage, tumor size, pathological stage (pStage), pathological nodal status, visceral pleural invasion, lymphovascular invasion, adjuvant therapy, recurrence, and survival (Table ). Additionally, for cases in which preoperative FDG‐PET of the primary tumor was performed, the SUVmax data were analyzed. 2.4 Tissue preparation and immunohistochemistry As described previously, H&E and cytokeratin AE1/3 immunohistochemistry slides for each tumor were prepared and used in this study. Tissue slides were scanned using a NanoZoomer 2.0 system (Hamamatsu Photonics, Hamamatsu, Japan). 2.5 Image segmentation and analysis The scanned pathological images of the tumors were reviewed by two pathologists (T.T. and G.I.). Whole tumor areas were annotated on cytokeratin AE1/3 immunohistochemistry slides using QuPath® software. For the segmentation of cancer cells in AE1/3 immunohistochemistry slides, a machine learning‐based method, simple linear iterative clustering (SLIC), was utilized through the skicit‐image and openslide modules in Python 3.8.1 (Figure ). The background and air space areas in the lung tissues were also segmented by binarizing the AE1/3 immunohistochemistry images. Finally, we integrated the segmented images and annotation data to create maps depicting the whole tumor area, cancer cells, cancer stroma, normal lung tissues, air space, and background for each case (Figure ; cancer cells: red, cancer stroma: yellow, the whole tumor area: red and yellow, normal lung tissue: gray, air space and background: black). In this study, we applied a mathematical approach to precisely analyze the spatial distribution of cancer cells and stroma. Entropy is a theory used to analyze data in various fields, including information computation, biology, and ecology. Shannon's entropy is a well known index for biodiversity in ecology. The TSR and Shannon's entropy were calculated as previously described. , For pathological image analysis, spatial entropy, a modified Shannon's entropy index weighted by the Euclidean distance of the objects has been proposed and utilized to investigate the spatial distribution of immune cells in cancer tissues. , Therefore, to properly characterize the spatial positional information in pathological images, we employed this framework to analyze the spatial distribution of cancer cells and stroma. More specifically, we multiplied Shannon's entropy of cancer cells and cancer stroma by the following coefficients: the sum of the Euclidean distance among cancer cell regions (Distance tumor ) and that among stroma regions (Distance stroma ) divided by the Euclidean distance of the whole tumor area (Distance all ). We defined this index as the STSDI, and clinicopathological analysis was conducted based on the STSDI (Figure ). In our analysis, we automated STSDI calculations by inputting annotated pathological images and their associated data directly into our analytical pipeline, streamlining processing and enhancing reproducibility. 2.6 Evaluation of growth patterns and tumor budding of squamous cell carcinoma The proportion of non‐destructive growth patterns of cancer cells in lung squamous cell carcinoma was also analyzed, as previously described. , In summary, cancer cell nests display replacement growth of alveolar‐lining epithelial cells or fill the alveolar space as non‐destructive growth patterns. As previously described, small isolated tumor clusters consisting of fewer than five cancer cells in the surrounding stroma are regarded as tumor budding. The proportions of non‐destructive and destructive growth patterns and the presence of tumor budding in each tumor were analyzed by two pathologists (T.T. and G.I.). 2.7 Statistical analysis Recurrence‐free survival (RFS), disease‐specific survival (DSS), and overall survival (OS) were analyzed using log‐rank tests. Survival curves were plotted using the Kaplan–Meier method. For the predictors of RFS and DSS, univariate and multivariate analyses were performed using the Cox proportional hazards regression model to calculate the 95% confidence intervals and hazard ratios (95% CI, HR). Two‐category comparisons were conducted employing either the chi‐square test, Fisher's exact test, or the Mann–Whitney U ‐test ( p < 0.05). All statistical analyses were performed using Python 3‐8‐1. 2.8 Ethical considerations This study was approved by the National Cancer Ethical Review Board (IRB approval number: 2019–091), and was performed in accordance with the tenets of the Declaration of Helsinki.   Patient selection The patient cohort and tissue specimens have been described previously. In short, 132 patients with peripheral lung squamous cell carcinoma measuring 3–5 cm who received surgical resection at the National Cancer Center Hospital East between 2002 and 2015 were enrolled. Cases that received neoadjuvant therapy were not included in this study.   Histological diagnosis Disease stages were classified according to the 8th edition of the TNM classification of malignant tumors. Histological diagnosis was made following the 5th edition of the World Health Organization series on histological classification.   Clinicopathological characteristics The clinicopathological characteristics of the patients were collected from the available medical records. The following factors were retrospectively analyzed: sex, age, smoking history, clinical stage, tumor size, pathological stage (pStage), pathological nodal status, visceral pleural invasion, lymphovascular invasion, adjuvant therapy, recurrence, and survival (Table ). Additionally, for cases in which preoperative FDG‐PET of the primary tumor was performed, the SUVmax data were analyzed.   Tissue preparation and immunohistochemistry As described previously, H&E and cytokeratin AE1/3 immunohistochemistry slides for each tumor were prepared and used in this study. Tissue slides were scanned using a NanoZoomer 2.0 system (Hamamatsu Photonics, Hamamatsu, Japan).   Image segmentation and analysis The scanned pathological images of the tumors were reviewed by two pathologists (T.T. and G.I.). Whole tumor areas were annotated on cytokeratin AE1/3 immunohistochemistry slides using QuPath® software. For the segmentation of cancer cells in AE1/3 immunohistochemistry slides, a machine learning‐based method, simple linear iterative clustering (SLIC), was utilized through the skicit‐image and openslide modules in Python 3.8.1 (Figure ). The background and air space areas in the lung tissues were also segmented by binarizing the AE1/3 immunohistochemistry images. Finally, we integrated the segmented images and annotation data to create maps depicting the whole tumor area, cancer cells, cancer stroma, normal lung tissues, air space, and background for each case (Figure ; cancer cells: red, cancer stroma: yellow, the whole tumor area: red and yellow, normal lung tissue: gray, air space and background: black). In this study, we applied a mathematical approach to precisely analyze the spatial distribution of cancer cells and stroma. Entropy is a theory used to analyze data in various fields, including information computation, biology, and ecology. Shannon's entropy is a well known index for biodiversity in ecology. The TSR and Shannon's entropy were calculated as previously described. , For pathological image analysis, spatial entropy, a modified Shannon's entropy index weighted by the Euclidean distance of the objects has been proposed and utilized to investigate the spatial distribution of immune cells in cancer tissues. , Therefore, to properly characterize the spatial positional information in pathological images, we employed this framework to analyze the spatial distribution of cancer cells and stroma. More specifically, we multiplied Shannon's entropy of cancer cells and cancer stroma by the following coefficients: the sum of the Euclidean distance among cancer cell regions (Distance tumor ) and that among stroma regions (Distance stroma ) divided by the Euclidean distance of the whole tumor area (Distance all ). We defined this index as the STSDI, and clinicopathological analysis was conducted based on the STSDI (Figure ). In our analysis, we automated STSDI calculations by inputting annotated pathological images and their associated data directly into our analytical pipeline, streamlining processing and enhancing reproducibility.   Evaluation of growth patterns and tumor budding of squamous cell carcinoma The proportion of non‐destructive growth patterns of cancer cells in lung squamous cell carcinoma was also analyzed, as previously described. , In summary, cancer cell nests display replacement growth of alveolar‐lining epithelial cells or fill the alveolar space as non‐destructive growth patterns. As previously described, small isolated tumor clusters consisting of fewer than five cancer cells in the surrounding stroma are regarded as tumor budding. The proportions of non‐destructive and destructive growth patterns and the presence of tumor budding in each tumor were analyzed by two pathologists (T.T. and G.I.).   Statistical analysis Recurrence‐free survival (RFS), disease‐specific survival (DSS), and overall survival (OS) were analyzed using log‐rank tests. Survival curves were plotted using the Kaplan–Meier method. For the predictors of RFS and DSS, univariate and multivariate analyses were performed using the Cox proportional hazards regression model to calculate the 95% confidence intervals and hazard ratios (95% CI, HR). Two‐category comparisons were conducted employing either the chi‐square test, Fisher's exact test, or the Mann–Whitney U ‐test ( p < 0.05). All statistical analyses were performed using Python 3‐8‐1.   Ethical considerations This study was approved by the National Cancer Ethical Review Board (IRB approval number: 2019–091), and was performed in accordance with the tenets of the Declaration of Helsinki.   RESULTS 3.1 Patient characteristics The study cohort (Table ) included 116 males and 16 females with a mean age of 71 years (range: 54–86 years). All patients received lobectomy and pneumonectomy, and 28 received adjuvant therapy. The follow‐up period ranged from 50 to 116 months (median follow‐up period for surviving patients: 76 months). 3.2 Histological images and STSDI values Representative H&E‐stained images, AE1/3 immunohistochemistry images, and segmented region maps are shown in Figure . The STSDI values were 0.173 and 0.129 for cases 59 and 92, respectively (Figure : Case 59, 2 B: Case 92). Because of the nature of the calculation formula, STSDI and TSR were strongly related, and the scatter plot exhibited a distribution that closely followed a parabolic shape (Figure ). Although their Shannon entropy values were the same when the tumors had the same TSR (Figure ), even with similar TSR, the STSDI values were different between Case 107 (Figure ; STSDI: 0.169) and Case 115 (Figure ; STSDI: 0.156) (Figure ). In Case 59, the presence of dilated air spaces within the tumor (depicted as black in the segmentation image) may initially obscure interpretation. However, both Case 59 and Case 107 demonstrate a widespread distribution of tumor and stromal regions, characterized by a notable intermingling pattern. Consequently, both cases exhibit elevated STSDI values. We defined STSDI‐high tumor as a STSDI value > median STSDI value (0.1606), and STSDI‐low tumor as STSDI value ≦ median STSDI value (Figure ). Further analysis of the two groups was performed to determine the clinical significance of the STSDI in this cohort. Similarly, tumors with Shannon's entropy > the median Shannon's entropy value were classified as Shannon's entropy high, whereas the others were classified as Shannon's entropy low. “In our analysis, we automated the spatial distribution index (STSDI) calculations by inputting annotated pathological images and their associated data directly into our analytical pipeline, streamlining processing and enhancing reproducibility.” In our analysis, we automated the spatial distribution index (STSDI) calculations by inputting annotated pathological images and their associated data directly into our analytical pipeline, streamlining processing and enhancing reproducibility. 3.3 Clinicopathological characteristics of STSDI‐high and ‐low groups The clinicopathological characteristics of patients in the STSDI‐high and STSDI‐low groups are shown in Table . Overall, these two groups showed no significant differences in clinicopathological factors, including sex, age, smoking history, clinical stage, tumor size, pathological nodal status, pathological stage, lymphovascular invasion, visceral pleural invasion, tumor budding, or adjuvant therapy. Similarly, the analysis revealed no statistical difference in FDG‐PET SUVmax values between the groups (Figure ). 3.4 Correlations between STSDI and patient survival To elucidate the clinical significance of the spatial distribution of cancer cells and stroma in squamous cell carcinoma, we analyzed the correlations between STSDI and patient outcomes. Log‐rank analysis showed that the STSDI‐low group had a significantly shorter RFS than the STSDI‐high group (Figure ). The 5‐years RFS rates were 49.5% in the STSDI‐low group and 77.0% in the STSDI‐high group. Furthermore, disease‐specific, and OS were significantly shorter in STSDI‐low tumors than in STSDI‐high tumors (Figure ; 5‐years DSS: 53.6% vs. 81.5%, 5‐years OS: 39.0% vs. 67.0%). In contrast, there were no significant differences in RFS, DSS, and OS between the Shannon's entropy high and entropy‐low groups (Figure ). In addition, although there were no significant differences in RFS, DSS, and OS between the STSDI‐high and ‐low groups in pStage I, STSDI‐low tumors had significantly shorter RFS, DSS, and OS than STSDI‐high tumors in pStage II and III (Figure ). 3.5 Univariate and multivariate Cox regression analysis for patient survival To investigate the impact of the STSDI and other clinicopathological characteristics on patient outcomes in detail, COX regression analysis was performed (Tables and ). Univariate analysis showed that low STSDI in addition to pStage and adjuvant therapy was an unfavorable factor for RFS ( p < 0.005, HR 2.894 (1.552–5.396)). Moreover, multivariate analysis revealed that a low STSDI was an independent unfavorable factor ( p < 0.005, HR 2.668 (1.413–5.038)) as well as pStage ( p < 0.005, HR 4.638 (2.369–9.080)). In addition, STSDI, visceral pleural invasion, and pStage were found to be unfavorable factors for disease‐specific survival in the univariate analysis, and multivariate analysis showed that they were independent predictors of disease‐specific death (STSDI: p < 0.005, HR 3.057 (1.470–6.360), pleural invasion: p = 0.010, HR 2.360 (1.225–4.549), pStage: p < 0.005, HR 4.964 (2.432–10.132)). 3.6 Growth patterns of squamous cell carcinoma and STSDI Considering the histopathological aspects, it is conjectured that the spatial distribution of cancer cells and stroma reflects the growth patterns of cancer cells. As shown in Figure , Case 1 had a large area of non‐destructive growth patterns in the tumor (non‐destructive growth patterns: 40%) and exhibited a high STSDI of 0.165. However, even with a similar TSR in Case 88 shown in Figure , the whole tumor consisted of destructive growth patterns of cancer cells with fibrotic stroma (non‐destructive growth patterns: 0%), and the STSDI of this tumor was 0.156. For statistical analysis, the cases were divided into two categories based on the proportion of non‐destructive growth patterns: non‐destructive growth‐low, where the proportion was 20% or below, and non‐destructive growth‐high, where it exceeded 20%. The results showed that the former group, with a smaller proportion of non‐destructive growth patterns, had a significantly lower STSDI than the latter group, with a larger proportion of non‐destructive growth patterns (Figure ).   Patient characteristics The study cohort (Table ) included 116 males and 16 females with a mean age of 71 years (range: 54–86 years). All patients received lobectomy and pneumonectomy, and 28 received adjuvant therapy. The follow‐up period ranged from 50 to 116 months (median follow‐up period for surviving patients: 76 months).   Histological images and STSDI values Representative H&E‐stained images, AE1/3 immunohistochemistry images, and segmented region maps are shown in Figure . The STSDI values were 0.173 and 0.129 for cases 59 and 92, respectively (Figure : Case 59, 2 B: Case 92). Because of the nature of the calculation formula, STSDI and TSR were strongly related, and the scatter plot exhibited a distribution that closely followed a parabolic shape (Figure ). Although their Shannon entropy values were the same when the tumors had the same TSR (Figure ), even with similar TSR, the STSDI values were different between Case 107 (Figure ; STSDI: 0.169) and Case 115 (Figure ; STSDI: 0.156) (Figure ). In Case 59, the presence of dilated air spaces within the tumor (depicted as black in the segmentation image) may initially obscure interpretation. However, both Case 59 and Case 107 demonstrate a widespread distribution of tumor and stromal regions, characterized by a notable intermingling pattern. Consequently, both cases exhibit elevated STSDI values. We defined STSDI‐high tumor as a STSDI value > median STSDI value (0.1606), and STSDI‐low tumor as STSDI value ≦ median STSDI value (Figure ). Further analysis of the two groups was performed to determine the clinical significance of the STSDI in this cohort. Similarly, tumors with Shannon's entropy > the median Shannon's entropy value were classified as Shannon's entropy high, whereas the others were classified as Shannon's entropy low. “In our analysis, we automated the spatial distribution index (STSDI) calculations by inputting annotated pathological images and their associated data directly into our analytical pipeline, streamlining processing and enhancing reproducibility.” In our analysis, we automated the spatial distribution index (STSDI) calculations by inputting annotated pathological images and their associated data directly into our analytical pipeline, streamlining processing and enhancing reproducibility.   Clinicopathological characteristics of STSDI‐high and ‐low groups The clinicopathological characteristics of patients in the STSDI‐high and STSDI‐low groups are shown in Table . Overall, these two groups showed no significant differences in clinicopathological factors, including sex, age, smoking history, clinical stage, tumor size, pathological nodal status, pathological stage, lymphovascular invasion, visceral pleural invasion, tumor budding, or adjuvant therapy. Similarly, the analysis revealed no statistical difference in FDG‐PET SUVmax values between the groups (Figure ).   Correlations between STSDI and patient survival To elucidate the clinical significance of the spatial distribution of cancer cells and stroma in squamous cell carcinoma, we analyzed the correlations between STSDI and patient outcomes. Log‐rank analysis showed that the STSDI‐low group had a significantly shorter RFS than the STSDI‐high group (Figure ). The 5‐years RFS rates were 49.5% in the STSDI‐low group and 77.0% in the STSDI‐high group. Furthermore, disease‐specific, and OS were significantly shorter in STSDI‐low tumors than in STSDI‐high tumors (Figure ; 5‐years DSS: 53.6% vs. 81.5%, 5‐years OS: 39.0% vs. 67.0%). In contrast, there were no significant differences in RFS, DSS, and OS between the Shannon's entropy high and entropy‐low groups (Figure ). In addition, although there were no significant differences in RFS, DSS, and OS between the STSDI‐high and ‐low groups in pStage I, STSDI‐low tumors had significantly shorter RFS, DSS, and OS than STSDI‐high tumors in pStage II and III (Figure ).   Univariate and multivariate Cox regression analysis for patient survival To investigate the impact of the STSDI and other clinicopathological characteristics on patient outcomes in detail, COX regression analysis was performed (Tables and ). Univariate analysis showed that low STSDI in addition to pStage and adjuvant therapy was an unfavorable factor for RFS ( p < 0.005, HR 2.894 (1.552–5.396)). Moreover, multivariate analysis revealed that a low STSDI was an independent unfavorable factor ( p < 0.005, HR 2.668 (1.413–5.038)) as well as pStage ( p < 0.005, HR 4.638 (2.369–9.080)). In addition, STSDI, visceral pleural invasion, and pStage were found to be unfavorable factors for disease‐specific survival in the univariate analysis, and multivariate analysis showed that they were independent predictors of disease‐specific death (STSDI: p < 0.005, HR 3.057 (1.470–6.360), pleural invasion: p = 0.010, HR 2.360 (1.225–4.549), pStage: p < 0.005, HR 4.964 (2.432–10.132)).   Growth patterns of squamous cell carcinoma and STSDI Considering the histopathological aspects, it is conjectured that the spatial distribution of cancer cells and stroma reflects the growth patterns of cancer cells. As shown in Figure , Case 1 had a large area of non‐destructive growth patterns in the tumor (non‐destructive growth patterns: 40%) and exhibited a high STSDI of 0.165. However, even with a similar TSR in Case 88 shown in Figure , the whole tumor consisted of destructive growth patterns of cancer cells with fibrotic stroma (non‐destructive growth patterns: 0%), and the STSDI of this tumor was 0.156. For statistical analysis, the cases were divided into two categories based on the proportion of non‐destructive growth patterns: non‐destructive growth‐low, where the proportion was 20% or below, and non‐destructive growth‐high, where it exceeded 20%. The results showed that the former group, with a smaller proportion of non‐destructive growth patterns, had a significantly lower STSDI than the latter group, with a larger proportion of non‐destructive growth patterns (Figure ).   DISCUSSION In this study, we identified the novel pathological characteristic of the spatial distribution of cancer cells and stroma, and demonstrated its clinical significance in predicting patient prognosis in peripheral lung squamous cell carcinoma. To characterize the spatial distribution of cancer cells and the surrounding stroma, we performed machine learning‐based image segmentation using immunostained images of cytokeratin and incorporated them with texture features. A machine learning method, SLIC, enabled us to precisely evaluate cytokeratin AE1/3‐positive cancer cell component and negative cancer stroma. In the field of radiology, previous studies have shown that textural features, including Shannon's entropy in radiological images, can predict patient outcomes. , , Furthermore, texture analysis has recently been used to analyze pathological tissue images, including the distribution of immune cells and Ki67 expression in cancer cells. , These results prompted us to analyze the textural features of each tumor. Especially, for the quantification of the spatial distribution, we employed one texture feature, spatial entropy, because it allows us to include spatial information of the cancer elements by multiplying and dividing Shannon's entropy according to their Euclidean distance. By employing spatial entropy, previous studies evaluated the local spatial distribution of immune cells in breast cancer and pancreatic cancer tissues. , However, in this study, we utilized spatial entropy to comprehensively analyze the spatial relationship between cancer cells and the surrounding stroma. Additionally, our analysis was conducted by expanding it to a larger area to include all tumor regions. In the present study, STSDI was significantly correlated with both tumor recurrence and patient prognosis, whereas Shannon's entropy, which did not account for the positional information of cancer cells and stroma, did not show any significant association with the prognosis. These results indicate that not only the proportion of tumor elements, but also their spatial distribution is an important characteristic for the evaluation of cancer tissues. Furthermore, our results support the proposition that the spatial interaction between cancer cells and stroma in the tumor microenvironment is a determinant of tumor behavior. , In Stage II–III patients, STSDI proves instrumental in prognostic stratification, thus potentially aiding adjuvant therapy planning. While adjuvant therapy is typically recommended for these stages by current guidelines, its practical application is often influenced by variables such as patient age and existing comorbidities. Our method improves the accuracy of identifying patients who may require more aggressive therapeutic strategies. Moreover, our results showed that low STSDI was an independent predictor of tumor recurrence and disease‐specific death. Interestingly, although STSDI values were not associated with the pathological findings including pathological stage, lymphovascular invasion, visceral pleural invasion, and tumor budding, a low STSDI correlated with destructive growth patterns of peripheral lung squamous cell carcinoma. Considering that the destructive growth pattern predicts an unfavorable prognosis as reported in previous studies, including ours, , , it is postulated that STSDI reflects the malignant potential of cancer, which may explain the poorer patient outcomes in STSDI‐low patients. In contrast to Koike's study, the TSR was not found to be a prognostic factor in this study, possibly because of the difference in segmentation methods, particularly because necrotic regions were not considered in this study. The limitation of this study is that it was a retrospective study conducted at a single institution. Our investigation focused on peripheral squamous cell carcinoma rather than central tumor, this is because the frequent occurrence of obstructive pneumonia associated with central tumors make it challenging to precisely evaluate whole tumor area. Recent advances in digital pathology and computational image analysis have enhanced the visualization of diverse cancer elements. , , By leveraging these advances, our study introduces a novel framework for characterizing the spatial distribution of cancer cells and the surrounding stroma. Importantly, while most studies focused on local proximity between the cancer elements within small regions of interest (ROIs), , we attempted to analyze the entire tumor region on the tissue slide by incorporating a mathematical algorithm into the histological image analysis. In addition, our framework automates the computational analysis of spatial distributions, enabling pathologists to conduct analyses simply by inputting annotation data and histological images, without the need for specialized computer skills. We anticipate that this facilitates its practical use in routine diagnostics, indicating a promising path toward widespread clinical application. Our novel approach enabled us to predict the malignant behavior of peripheral lung squamous cell carcinoma. Further investigations are required to explore whether this approach can be effectively implemented for different cancer types; however, we believe that our approach provides a new perspective on the pathological analysis of cancer tissues.   Tetsuro Taki: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; visualization; writing – original draft; writing – review and editing. Yutaro Koike: Investigation; resources; writing – review and editing. Masahiro Adachi: Writing – review and editing. Shingo Sakashita: Writing – review and editing. Naoya Sakamoto: Writing – review and editing. Motohiro Kojima: Writing – review and editing. Keiju Aokage: Resources; writing – review and editing. Shumpei Ishikawa: Writing – review and editing. Masahiro Tsuboi: Resources; writing – review and editing. Genichiro Ishii: Conceptualization; investigation; writing – review and editing.   The authors thank editage (website) for English‐ language editing.   This work was supported in part by the Japan Society for the Promotion of Science KAKENHI grant number 21K20821.   T.T, Y.K., M.A., S.S., N.S., M.K., K.A. and M.T. declare no conflict of interest in association with the present study. S.I. and G.I. are editorial board members of Cancer Science.   Approval of the research protocol by an Institutional Review Board: Institutional review board approval was obtained from the participating institution (approval number: 2019–091), and the study was publicly disclosed on the official websites of the conducting institutions. Research participants were provided the opportunity to decline participation. This study adhered to the principles outlined in the Declaration of Helsinki. Informed Consent: N/A. Registry and the Registration No. of the study/trial: N/A. Animal Studies: N/A.   Figure S1. Figure S2. Table S1.   